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2010DataStage
:: FUNDAMENTAL CONCEPTS::
DAY 1
Introduction for Phases of DataStage
Four different phases are in DataStage, they are
Phase I: Data Profiling
It is for source system analyses, and the analysis are
1. Column analysis,
2. Primary key analysis,
3. Foreign key analysis, by this analysis whether we can find the data is “dirty” or “not”.
4. Base Line analysis, and
5. Cross domain analysis.
Phase II: Data Quality (or also called as cleansing) In this process we must follow inter dependent i.e., after one after one process as
shown below.
Parsing
Correcting Inter
Standardizing Dependent
Matching
Consolidated
Phase III: Data Transmission
In this ETL process is done here, the data transmission from one stage to another stage
And ETL means
E- Extract
T- Transmission
L- Load.
Phase IV: Meta Data Management - “Meta data means where the data for data”.
Navs notes Page 1
2010DataStage
DAY 2
How the ETL programming tool works?
Pictorial view:
Data Base
ETL Process Business Interface
Flat files
MS Excel
Figure: ETL programming process
Navs notes Page 2
db ETL BI DM
DWH
2010DataStage
DAY 3
Continue…
Extracting from .txt (ASCII code)
Source
Understand to DataStage Format (Native Format)
Source
Source
Loading the data into .txt (ASCII code) data base or resides in
local repository
ETL is a process that is performs in stages:
S T S T S T
OLTP stage area sa sa sa DWH
Here, S- source and T- target.
Home Work (HW): one record for each kindle (multiple records for multiple addresses and
dummy records for joint accounts);
Navs notes Page 3
Extract window
Load window
Staging (permanent data)
Staging (after transmission)
DWH
2010DataStage
DAY 4
ETL Developer Requirements
Q: One record for each kindle(multiple records for multiple addresses and dummy
records for joint accounts);
Kindle means information of customers.
Customer
Loan
Bank Credit card
Savings kindle
Customer maintaining one record but handling different addresses is called ‘single
view customer’ or ‘single version of truth’.
HW explanation: Here we must read the query very care fully and understand the terminology
of the words in business perceptive. Multiple records means multiple of the
customers(records) and multiple addresses means one customer(one account) maintaining
multiple of addresses like savings/credit cards/current account/loan.
ETL Developer Requirements:
HLD LLD ,, ,, ,,
Inputs
here,
HLD- high level document
Developer LLD- low level document
Navs notes Page 4
2010DataStage
ETL Developer Requirements are:
1. Under Standing
2. Prepare Questions : after reading document which is given and ask to friends/
forums/team leads/project leads.
3. Logical designs : means paper work.
4. Physical model : using Tool.
5. UNIT Test
6. Performance Tuning
7. Peer Reviews : it is nothing but releasing versions(version control *.**)
here, * means range of 1-9.
8. Design Turn Over Document (DTD)/ Detailed Design Document(DDD)/ Technical
Design Document(TDD)
9. Backups : means importing and exporting the data require.
10. Job Sequencing
Navs notes Page 5
2010DataStage
DAY 5
How the DWH project is under taken?
Process:
HLD
Requirements: Warehouse(WH) -HLD
x x
x TD jobs in %
Developer (70% - 80%)
as developer involves Developer system engineer Production(10%)
Migration (30%)
x TEST Production Migration
x x
here, x – cross mark that developer not involves in the flow.
– mean where the developer involves in the project and implement all TEN
requirements shown above.
Production based companies are like IBM and so on.
Migration means Support based companies like TCS, Cognizent,
Satyam Mahindra and so on.
In Migration: works both server and parallel jobs.
Server jobs – parallel jobs
Up to 2002 this environment worked after 2002 and up to till this environment
IBM launched X-Migrator, which convert server jobs to parallel jobs
In this it converts up to, 70% automatically
30% manually.
Navs notes Page 6
2010DataStage
Project divided into some category with respective to period as shown below and its
period( time of the project).
Categories - Period (that taken in months and years)
Simple 6m
Medium 6m – 1y
Complex 1– 11/2 y
Too complex 11/2 y – 5y and so on(it may takes many years depend up on project)
5.1. Project Process:
(high level documents)
HLD SRS (here, business analyzer/ Subject matter expert)
Requirements: BRD
HLD Architecture
Warehouse: Schema (structure)
Dimensions and tables (target tables)
Facts
(low level doc’s)
LLD Mapping Doc’s (specifications-spec’s)
TD Test Spec’s
Naming Doc’s
Navs notes Page 7
2010DataStage
5.2. Mapping Document:
For example if a query requirements are 1-experience employee, 2- dname, and 3- first
name, middle name, last name.
For this mapping pictorial way as we see in the way:
Common fields
S.no Load
order
Target
Entity
Target
Attributes
Source
Tables
Source
Fields
Transmi
ssion
Constan
t
Error
Handling
Eno Hire date
Dno
Current Date-
Hire date
(CD-HD)
Pk F
FName Ename Fk C
Exp_tbl MName Emp Eno Sk D
LName Dept Dno C
Exp_emp Dname
DName
Funneling
S1
Target
S2
Horizontal combining
or vertical combining
Navs notes Page 8
Get data from Multiple tables ‘C’Is combining
2010DataStage
As per example here horizontal combination is used
Emp
Trg
Dept
Here, HC means Horizontal combination is used for combine primary rows with secondary
rows.
As Developer maximum 30 Target fields will get.
As Developer maximum 100 source fields will get.
“Look Up!” means cross verification from primary table.
After document:
.txt (fwf, cv, vl, sc, s & t, h & t)
( F/ dB) (Types of dB)
S1
S2
Format of Mapping Document.
Navs notes Page 9
HC
HC T HC
TRG
2010DataStage
DAY 6
Architecture of DWH
For example: dB every branch have each mgr
Manager
Reliance comm.
Reliance Group : manager Top Level mgr(TLM)
Reliance power details of below
sales
Manager customer
Reliance Fresh TLM needs employee
period
` order
Input
Explanation of above example: Reliance group with some there branches and every branch
have one manager. And for all this manager one Top level manager (TLM) will be there. And
TLM needs the details of list shown above for analyze. Bottom level
For above example how ETL process is done shown below RC-mgr
reliance fresh ERP mini WH/
Data mart
DWH
independent Data Mart Dependent Data Mart
Reliance Fresh(taking one from group directly)
Navs notes Page 10
ETLPROCESS
ETLPROCESS
2010DataStage
Dependent Data Mart: means the ETL process takes all manager information or dB and keep
in the Warehouse. By that the data transmission between warehouse and data mart where
depends upon by each other. Here Data mart is also called as ‘Bottom level’/ ‘mini WH’ as
shown in blue color in above figure i.e., the data of individual manager (like RF, RC, RP and
so on). Hence the data mart depends up on the WH is called dependent data mart.
Independent Data Mart: only one or individual manager i.e., data mart were directly access the
ETL process with out any help of Warehouse. That’s why its called independent data mart.
6.1 Two level approaches:
For the both approaches two layers architecture will apply.
1. Top-Bottom level approach, and
2. Bottom- Top level approach.
6.1. Top – Bottom level approach:
The level start from top means as per example Reliance group to their individual
managers their ETL process from their to Data Warehouse (top level) and from their to all
separate data marts (bottom level).
R Comm. Data Mart
R Power Data Mart
Reliance Group
Warehouse
R Fresh Data Mart
Top level Bottom level
Layer I Layer II
Top – Bottom level approach
Navs notes Page 11
ETL PROCESS
2010DataStage
In the above the top – bottom level is defined, and this approach is invented by W. H. Inner.
Here, warehouse is top level and all data mart are bottom level as shown in the above figure.
6.2. Bottom – top level approach:
Means from here the ETL process takes directly from data mart (DM) and the data put
in the warehouse for reference purpose or storing the DM in the Data WareHouse (DWH).
R comm.
DM
R power
DM DWH
Reliance Group
R fresh DM
Layer I Layer II
Bottom level Top level
Bottom – Top level approach is invented by R Kimbell.
Here, one data mart (DM) contains information like customer, products, employees, location
and so on.
Top – Bottom level approach
These two approaches comes under two layer Architecture
Bottom – Top level approach
Navs notes Page 12
ETL PROCESS
2010DataStage
Programming (coding)
ETL Tool’s: GUI(graph user interface)
This tool’s to “extract the data from heterogeneous source”.
ETL program Tool’s are “Tara Data/ Oracle/ DB2 & so on…”
6.2. Four layers of DWH Architecture:
6.2.1. Layer I:
DM
DM
Source DWH Source
DMLayer I Layer I
In this layer the data send directly in first case from source to Data WareHouse(DWH) and in
second case source to group of Data Marts(DM).
6.2.2. Layer II:
DM DM
SRC DWH SRC DWH
DM DM
Layer I Layer II Layer I Layer II
Navs notes Page 13
2010DataStage
TOP – BOTTOM APPROACH BOTTOM – TOP APPROACH
In this layer the data follow from source – data warehouse – data mart and this type of follow
is called “top – bottom approach”. And in another case the data follow from source – data
marts – data warehouse and this type of following data is called “bottom – top approach”. For
this Layer II architecture is explained in the above shown example eg. Reliance group.
* (99.99% using layer 3 and layer 4)
6.2.3. Layer III:
DM
Source ODS DWH DM
DM Layer I Layer II Layer III
In this layer the data follow from source – ODS (operations data stores) – DWH – Data Marts.
Here the new concept add that is ODS means operations of data stores for at period like 6
months or one year that data used to solve instance problem where the ETL developer is not
involved here.
And who solve the instance/ temporary problems that team called Interface team is
involved here. The ODS data stores after the period into the DWH and from that it goes to DM
there the ETL developers involves here in layer 3.
The clear explanation about the layer 3 architecture in the below example, it is the best
example for clear explanation.
Navs notes Page 14
2010DataStage
Example #1:
Source (it is waiting for landing, because of some technical problem)
(at least or max. 2hrs to solve the problem )ETL dev. Involves here
Layer I
DM
DM
Airport terminal DWH
Interface team involves here Stores problem info for future references DM
Layer III
Layer II
Problem information captured
Data Base (stores the technical problem in dB for 1year)OPERATIONS DATA STORE
Example explanation:
In this example, source is aero plan that is for waiting for landing to the airport
terminal. But it is not to suppose to land because of some technical problem in the airport base
station. To solve this type operations special team involves here i.e., interface team. In the
airport base station the technical problems and the Operations Data Store (ODS) in db i.e.,
simple say problem information captured.
But the ODS stores the data for one year only. And years of database stores in the data
warehouse because of some technical problems to be not repeat or for future reference. From
Navs notes Page 15
Airport base station
ODS
2010DataStage
DWH to it goes to Data Marts here ETL developers involves for solve technical problems i.e.,
is also called layer 3 architecture of data warehouse.
DAY 7
Continues…..
Project Architecture:
7.1. Layer IV: Layer 4 is also called as “Project Architecture”
It is for data backup of DWH & SVC
L3
Business intelligence
Read flat files through DS
L2 L4
Reporting
Layer I
SRC Figure: Project Architecture / layer IV
Here,
ODS-operations data store,
DW- Data Warehouse,
DM- Data Mart,
SVC- Single view customer,
BI- Business Intelligence.
Navs notes Page 16
Source1
Source2
Source3
Interface
Files
(FLAT FILES)
ETL
ODS SVC DM
DW
BI DM
Condition
MISMATCH
Format
MISMATCH
lookup
2010DataStage
L2 & L3 & L4- layer2,3,4.
------------- reference data
- - - - - - - -> reject data
About the project architecture:
In project architecture, there are 4 layers.
In first layer source to interface files(flat files),
Coming to second layer ETL reads the flat files through the DataStage(DS) and sends
to ODS. When ETL sending the flat files to ODS if any mismatch data will there it will
drops that data. There are two types mismatch data 1. Condition mismatch 2. Format
mismatch.
In third layer the ETL transfer the data to warehouse.
In last layer data warehouse to check whether a single customer or not and data loading
or transmission in between DWH and DM(business intelligence).
Note: (Information about dropped data when the transmission done between ETL reads the flat
files(.txt, csv, .xml and so on) to ODS.)
Two types of mismatch data:
Condition mismatch(CM) : this verify the data from flat files whether they are
conditions are correct or mismatched, if it is mismatched the record will drops
automatically. To see the drop data the reference link is used and it shows which
record is condition mismatched.
Format mismatch(FM) : this is also like condition mismatch but it checks on the format
whether the sending data or records is format is correct or mismatched. Here also
reference link is used to see drop data.
Example for condition mismatch: An employee table contains some data
SQL> select * from emp;
EID ENAME DNO
08 Naveen 10
Navs notes Page 17
emp tbl
TRG
Contains
dno 10,20,30,10
Trg only req. dno = 10
2010DataStage
19 Munna 20
99 Suman 30
15 Sravan 10
Example for Format Mismatch:
Here the table format is tab – space separated.
The cross mark record has format mismatched so that the
record its just rejected.
7.2. Single View Customer (SCV):
It is also called as “single version of truth”.
For example:
*to make unique customer?
Same records
Phase – II > identify field by field.
Phase – III> cannot identify in this.
5 multiple records of customers
transforming
Here DataStage people involves in this process
Navs notes Page 18
drops20,30
from emp
Reference link
EID EName Place
111 naveen mncl
222 munna knl
333suman hyd
CName Adds.
naveen savingsmunna insurancesuman creditnaveen loanmunna deposit
CName Adds.
naveen savings, loanmunna insurance, depositsuman credit
2010DataStage
SVC/ single version of truth
This type of transforming is also called as Reverse Pivoting.
NOTE: Business intelligence(BI DM) is for data backup of DWH & SVC(single version of truth).
DAY 8
Dimensional Model
Modeling: it represent in physical or logical design from our source system to target system.
o Logical design: client perspective,
o Physical design: data base perspective.
optional
Manual
Here the above is Designing manual
Data Modeler’s are use DM Tools
o ERWIN Forward Engineering
o ER – STUDIO Reverse Engineering
Entity relation windows (ERWIN),
Entity relation studio(ER-Studio) these two are data modeler’s where logical and
physical design is done.
Mata Data : every entity has a structure is called Meta Data(simple say ‘a data to a
data’)
o In a table there are attributes and domain, two types of domain they are 1.
Alphabetical and 2. Number.
Navs notes Page 19
Pictorial View Logical View
EMP Dept
SQ B
2010DataStage
Forward Engineering (FE): it’s starting from the scratch.
Reverse Engineering (RE): it’s create from existing system is known as RE. are simple
say “ altering the existing process”
For example:
Q: An client required a experience of an employee.
SRC
EMP_table
Implicit requirement (is experience of employee) Hire Date
From Developer point of view is
Explicit Requirement (to find out everything as per the client requirement want to see)
TRG
Lowest level Detailed Information
8.1. Dimensional Table:
To find out everything as per the client requirement want to see (or) the “Lowest level
of detailed information” in the target tables is called Dimensional Table.
Q: how the tables are interconnected is shown below.
- Here taking some tables and linking with them with related to other tables.
- Like in product table.
Navs notes Page 20
(Employee hire detailed information)ENo EName Years Months Days Hours Minutes Seconds Nano_Seconds
2010DataStage
- This link is created by using foreign key and primary key.
- Foreign Key: means which is constraint and used as reference for other table.
- Primary Key: means which is constraint, it is a combination of unique and not null.
- Surrogate key.
- Tables as follow.
Foreign Key
Primary Key Foreign Key Link Establishing
Using Fk & Pk
Primary Key
8.2. Normalized Data:
In a table there if repetitive information or records is called Redundancy, that information
is to minimize or that technique is called as Normalization.
For example:
These is
Repetitive
Information
or Redundancy
Fk Pk
Navs notes Page 21
Product_ID PRD_Desc PRD_TYPE_ID
PRD_TYPE_ID PRD_Category PRD_SP_ID
PRD_SP_ID SName ADD1
ENO EName Designation DNo Higher Quali. Add1 Add2
111 naveen ETL Developer 10 M.TECH JNTU HYD222 munna System analysis 20 M.SC SVU HYD333 Sravan JAVA Developer 10 M.TECH JNTU HYD444 Raju Call Center 30 M.SC KU WRL555 Rajesh JAVA Developer 10 M.TECH JNTU HYD666 Hari .NET Developer 20 M.SC SVU HYD777 Mahesh Bank PO 10 M.TECH JNTU HYD
ENO EName Designation DNo
111 naveen ETL Developer 10 222 munna System analysis 20333 Sravan JAVA Developer 10 444 Raju Call Center 30555 Rajesh JAVA Developer 10666 Hari .NET Developer 20 777 Mahesh Bank PO 10
DNo Higher Quali. Add1 Add2
10 M.TECH JNTU HYD20 M.SC SVU HYD30 M.SC KU WRL
2010DataStage
This dividing
Technique
is known
Normalization
(or) reducing
Redundancy
The target table must be always in De-Normalized format.
Normalization De-Normalization
De-Normalization means combining the multiple tables into one table. And
combining is done by Horizontal combine.
But it is not in all cases, that de-normalized is must and should.
Navs notes Page 22
HC
2010DataStage
DAY 9
E-R Model
An Entity-Relationship Model:
In logical design, there are two options to design a job. They are
1. Optional, and
2. Manual.
Mandatory is must 1- primary table & n-secondary table
EMP table DEPT table
The given two tables EMP and DEPT
Primary (or also known as Master Table)
Secondary (or also known as Child Table)
Navs notes Page 23
ENO EName Designation DNo
111 naveen ETL Developer 10 222 munna System analysis 20333 Sravan JAVA Developer 10 444 Raju Call Center 30555 Rajesh JAVA Developer 10666 Hari .NET Developer 20 777 Mahesh Bank PO 10
DNo Higher Quali. Add1 Add2
10 M.TECH JNTU HYD20 M.SC SVU HYD30 M.SC KU WRL
2010DataStage
Here from above two tables the primary table is DEPT table, because is not depends
for any other table. And EMP table is secondary table because it is depends on the
DEPT table.
But when we take in real time, that we joining the two table by using Horizontal
combining it takes the EMP table as primary table and DEPT table as secondary table.
9.1. Horizontal Combine:
To perform horizontal combining we must follow these cases.
It must have multiple sources.
There should be dependency.
1 – Primary, n – secondary.
Horizontal combining is also called as JOIN.
HC means combining primary rows with secondary rows based on the primary key
column values.
There are three types of keys, they are
o Primary key,
o Foreign key, and
o Surgut key.
For example combining these two tables: EMP & DEPT tables
Fk Pk
Navs notes Page 24
ENO EName Designation DNo
111 naveen ETL Developer 10 222 munna System analysis 20333 Sravan JAVA Developer 10 444 Raju Call Center 30555 naveen ETL Developer 10666 munna System analysis 20 777 naveen ETL Developer 10
DNo Higher Quali. Add1 Add2
10 M.TECH JNTU HYD20 M.SC SVU HYD30 M.SC KU WRL
2010DataStage
After combining or joining the table by using HC, hence it’s like below
9.2. Different types of Schema’s:
There are four types schemas, they are
o STAR Schema,
o Snow Flake Schema,
o Multi STAR Schema, and
o Galaxy Schema.
1. STAR Schema:
In the star schema, you must know about two things
o Dimensional table, and
o Fact table.
Dimensional table: means ‘Lowest level detailed information’ of a table.
Fact Table: means it is collection of foreign key from n- dimensional tables.
Definition of STAR Schema:
“A Fact Table collection of foreign key surrounded by multiple dimensional table and
each dimensional collection of de-normalized data, it is called STAR Schema.”
The data transmission is done in two different methods,
in pictorial way it look like as below
Transmission
Navs notes Page 25
ENO EName Designation DNo Higher Quali. Add1 Add2
111 naveen ETL Developer 10 M.TECH JNTU HYD 222 munna System analysis 20 M.SC SVU HYD333 Sravan JAVA Developer 10 M.TECH JNTU HYD444 Raju Call Center 30 M.SC KU WRL
Source DIM table
FACT tblT T
2010DataStage
in practical way it directly from source to dimensional table and fact table.
Example for STAR Schema:
“As taking some tables as below to derive a star schema from that”.
Q: display what suman buy a lux product in ameerpet on January 1st week?
Bridge/ intermediate table
Product table
Brand table
Category table Pk Pk
Customer table
Unit table
Customer_Category_table
Customer table
Location table Pk Pk
Here, Pk – primary key, and
Fk – foreign key.
By above shown that fact table is surrounded by the dimensional table, and fact table is
collection of foreign key, where dimensional table is lowest level detailed information.
And fact table is also called as Bridge or Intermediate table.
But in current market STAR Schema and Snow Flake Schema is using rarely.
Navs notes Page 26
Source
FACT tbl
DIM tableT
T
PRD_Dim_tbl
Cust_Dim_tbl
Date_Dim_tbl
Loc_Dim_tbl
Fact tableFk FkFk Fkmeasurements
2010DataStage
In the fact table, measurements mean taking the information as per client requirement
or user requirement.
As per above question, it needs information PRD_dim_tbl, Date_dim_tbl,
Cust_dim_tbl, and Loc_dim_tbl. The link is creating to the measurements i.e., for Fact
table by foreign key and primary key.
2. Snow Flake Schema:
The fact-tables surrounded by dimensional table, each dimensional table have
lookup table is called Snow Flake Schema.
For example:
Fk Pk Fk Pk Fk Pk
If we want to require the information from location table it fetch from that table and
display the client required.
To minimize the huge data at once or in a one dimensional table, some times it not
possible to bring as soon as possible if huge data in dimensional table.
That is reason we divide the dimensional table, into some tables. And that tables is
known as “look up tables”
Definition of Snow Flake Schema:
“The Fact table surrounded by dimensional tables, and each dimensional table have
look up tables is called Snow Flake Schema”.
STAR Schema works effectively
De-normalization
Navs notes Page 27
EMP_tbl
Dept_tbl
Locations
Area
Cagnos/BO
DN
2010DataStage
Reports
Normalization
Snow Flake Schema works effectively
NOTE: Selection of Schema in run time it is depends on report generation.
:: DataStage CONCEPTS::
DAY 10
DataStage (DS) Concepts:
History of DS,
Feature of DS,
Differences between 7.5.x2 and 8.0.1 versions,
Architecture of 7.5.x2 and 8.0.1 versions,
Enhancements and new features of version 8.0.1
HISTORY of DataStage
An ETL tool according year 2006 there are 600 tools in market, some of they are
DataStage Parallel Extends,
ODI(OWB),
SAS(ETL Studio),
BODI,
Abinity and so on…
But DataStage is so famous and widely used in the market and it is to expensive also.
Navs notes Page 28
Source
DWH
MIG/H1
N
Q: What is DataStage?
ANS: DataStage is a comprehensive ETL tool, which provides End – to – End Enterprise
Resource Planning (ERP) solution (here, comprehensive means good in all areas)
2010DataStage
History begins:
- In 1997, first version of DataStage is released by VMARK company i.e., US based
company, and the Mr. LEE SCHEFFLER is father of DataStage.
- Only 5 members involved in release the software into the market.
- DataStage those days called as “Data Integrator”.
- There are 90% changes from 1997 to 2010 comparing to release versions.
- In 1997, Data Integrator is acquiring by company name called TORRENT.
- After two years i.e., in 1999, INFORMIX Company has acquired Data Integrator from
TORRENT Company.
- In 2000, ACENTIAL Company acquired both Data Base and Data Integrator and after
that ACENTIAL DataStage Server Edition released in this year.
o By this company the DataStage has popularized into the market from that year.
o And released software were 30 tools used to run.
- In 2002, ADSS + ORCHESTRATE means ACENTIAL company is integrated with
ORCHESTRATE company for the parallel capabilities.
o Because ORCHESTRATE (PX, UNIX) have parallel extendable capabilities in
UNIX environment.
o By integrating ADSS + ORCHESTRATE and they named as ADSSPX.
o And ADSSPX is version is 6.0, from that version parallel operations starts or
parallel capabilities starts.
o From that parallel versions gone on developing up to 7.5.1 version,
o But from 6.0 to 7.5.1 versions they supports only UNIX flavors environment.
o Because server configured only on UNIX flat form or environment.
- In 2004, a version 7.5.x2 is released that support server configuration for windows flat
form also.
Navs notes Page 29
2010DataStage
o For this ADSSPX is integrated with MKS_TOOL_KIT.
o MKS_TOOL_KIT is virtual UNIX machine that brings the capabilities to
windows for support server configuration.
o NOTE: After installing the ADSSPX+MKS_TOOL_KIT into the windows, and
all the UNIX commands works in the windows flat form.
- In 2004, December the version 7.5.x2 were having ASCENTIAL suite components
o They are,
Profile stage,
Quality stage,
Audit stage, these are individual tools.
Meta stage,
DataStage Px,
DataStage Tx,
DataStage MUS, and so on
o There are 12 types of ASCENTIAL suite components.
- In 2005, February the IBM acquired all the ASCENTIAL suite components and the
IBM released IBM DS EE i.e., enterprise edition.
- In 2006, the IBM has made some changes to the IBM DS EE and the changes are the
integrated the profiling stage and audit stage into one, quality stage, Meta stage, and
DataStage Px.
o With the combination of four stages they have released
“IBM WEBSPHERE DS & QS 8.0”
o This is also called as “Integrated Developer Environment” i.e., IDE.
Navs notes Page 30
2010DataStage
- In 2009, IBM has released another version that “IBM INFOSPHERE DS & QS 8.1”
o In current market,
7.5.x2 using 40 – 50%
8.0.1 using 30 – 40%
8.1 using 10 – 20%
DAY 11
DataStage FEATURES
Features of DS:
There are 5 important features of DataStage, they are
- Any to Any,
- Plat form Independent,
- Node configuration,
- Partition parallelism, and
- Pipe line parallelism.
Any to Any:
o DataStage that capable to any source to any target.
Plat form Independent:
o “A job can run in any processor is called plat form independent”
o Three types of processor are there, they are
UNI,
Symmetric Multi Processor(SMP), and
Massively Multi Processor (MMP).
UNI SMP MMP
Navs notes Page 31
NOTE: DataStage is Front End, it nothing to be stored.
2010DataStage
“ “ “
”””
RAM RAM
Node Configuration:
o Node is software that is created in operating system.
o “Node is a logical CPU i.e., is instance of physical CPU.
o Hence, using software it is “the process of creating virtual CPU’s is called
Node Configuration.”
o Node configuration concept is exclusively work on the DataStage, it is the best
feature comparing from other ETL tools.
o For example:
An ETL job requires executing 1000records?
For above question an UNI processor takes 10mins to execute 1000
records.
But for the same question an SMP processor takes 2.5 minutes to
execute 1000 records.
It is explained clearly in below diagram.
1000 records 1000 records
Here,1000 records share
for four CPU’ hence
Navs notes Page 32
HDD
C
PU
HDD
C
PU
C
PU
C
PU
C
PU
SMP -1 SMP -2
SMP -3 SMP -n
C
PU
HDD HDD
C
PU
C
PU
C
PU
C
PU
2010DataStage
execution time reduced.
10 minutes 2.5 minutes
RAM RAM
o As per above example, Node Configuration is also can create virtual CPU’s to
reduce the execution time for UNI processor.
o Using Node Configuration for the above example to UNI processor.
o In below figure how the virtual CPU’s can create and reduce the execution time
of the process.
1000 records created multiple nodes
Node1
Node2
Node3
Node4
10 minutes reduces to 2.5minutes
RAM
Partition parallelism:
o Partition is a distributing the data across the nodes, based on partition
techniques.
o Considering one example why we use the partition technique’s
o Example: taking some records in EMP table and some in DEPT table
EMP table have 9 records,
DEPT table have 3 records.
Navs notes Page 33
C
PU
HDD
CPU
PUCPU
PU
CPU
PU
CPU
PU
2010DataStage
o After partitioning these records output must and should have 9 records, because
here primary table is 9 records.
EMP(10,20,10,30,20,10,10,20,30) and DEPT(10,20,30)
N1 10,20,10 10 only 2 matched
12 N2 30,20,10 20 1 total only 4 matched
But output must be 9 records
N3 10,20,30 30 1
o In the above example, only 4 records are in there in final output and 5 records
are missing for this reason the partition techniques are introduced.
o And there are two types of partition parallelism categories, in those total 8
types of partition techniques are there.
Key based
Hash
Modulus
Range
Db/2
Key less
Same
Random
Entire
Round robin
o Key based category or key based techniques will give the assurance, to the
same key column value to collected same key partition.
o Key less technique is used to append the data for joining given tables.
From above taken records we partitioning using key based.
Navs notes Page 34
4
2010DataStage
Key based partitioning
EMP DNO N1 10
N2 20
DEPT N3 30
DAY 12
Continues…
Features of DataStage
Partition Parallelism:
o Re – Partition: means re – distributing the distributed data.
P1
P2
P3
EMP 10 N1 N1 AP
20 N2 N2 TN
Dno
DEPT 30 N3 N3 KN
Navs notes Page 35
JOIN
JOIN
ENO EName DNo Loc
111 naveen 10 AP222 munna 20 TN333 Sravan 10 KN 444 Raju 30 AP555 naveen 10 TN666 munna 20 TN777 naveen 30 KN
2010DataStage
Dno Loc
o First partition is done by key based partition for dno, and taking a separate
column as location, for that it re – distributing for the distributed data. i.e.,
known as Re – Partition.
o Reverse Partitioning:
It is also called as collecting. But it done in one case only or in one
situation only : “when the data move from parallel stage to sequential
stage the collecting happens in this case only”
Designing job in “stages”
is also called as link or pipe, this is channel it is moving data from
one stage to another stage.
S1 S2 S3
Example:
Here collecting to Nodes
N1 N
Nn
Parallel files into Sequential/Single file
There are four categories of collecting techniques
Order
Navs notes Page 36
SRC TRSF TRG
S1 S2
2010DataStage
Round robin
Sort – merge
Auto
Example for collecting techniques:
N1 a,x
N2 b,y N
N3 c,z
Pipeline Parallelism:
“All pipes carry the data parallel and the process done simultaneously”
o In server environment: the execution process is called traditional batch
processing.
o For example: how the execution done in server environment we see
Extract Transform Load
10min 10min 10min
Here, the execution taken 30minutes to complete.
o Same job in parallel environment :
E T L R5 R3 R1
R4 R2
Navs notes Page 37
Order RR SM Auto
a a a ax b b zb c c yy x x cc y y xz z z b
S1
S2
S3
HD HD
S1
S2
S3
2010DataStage
Here, all the pipe carry the data parallel and processing the job
simultaneously and the execution taken only 10 minutes to complete
By using the pipeline parallelism we can reduce the process time.
DAY 13
Differences between 7.5.x2 & 8.0.1
Differences:
7.5.x2 8.0.1
- 4 client components - 5 client components
* DS Designer * DS Designer
* DS Manager * DS Director
* DS Director * DS Administrator
* DS Administrator * Web Console
* Information Analyzer
- Architecture Components - Architecture Components
* Server Component * Common User Interface
* Client Component * Common Repository
* Common Engine
* Common Connectivity
* Common shared Services
Navs notes Page 38
7.5.x2 8.0.1
2010DataStage
- II- tier architecture - N-Tier architecture
- OS dependent w.r.t. users - OS independent w.r.t. users but one
time dependent only.
- Capable of P-III & P-IV - Capable of all phases.
- No web based administration - Web based administration through
web console( simple say work from home)
- File based repository - Data Base based repository
13.1. Client components of 7.5.x2:
- DS Designer: it is to create jobs, compile, run and multiple job compile.
4 types of jobs can handle by DS Designer.
Mainframes job
Server job
Parallel job
Job sequence job
- DS Director: it can handle the given list below.
Schedule , run job’s
Monitor, Unlock, batch jobs
Views(job, status, logs)
Message Handling.
- DS Manager: it can handle the given list below.
Import and Export of Repository components
Node Configuration
- DS Administrator: it can handle the given list below.
Create project
Delete project
Organize project
Navs notes Page 39
2010DataStage
13.1. Client components of 8.0.1:
- DS Designer: it is to create jobs, compile, run and multiple job compile.
4 types of jobs can handle by DS Designer.
Mainframes job
Server job
Parallel job
Job sequence job
Data quality job
- DS Director : same in as above shown in 7.5.x2
- DS Administrator : same in as above shown in 7.5.x2
- Web Console : administrator components through which performing.
Security services
Scheduling services
Logging services
Reporting services
Session management
Domain manager
- Information Analyzer : is also called as console for IBM INFO SERVER.
It perform all phase-I activities
Column analysis,
Primary key analysis,
Foreign key analysis,
Base Line analysis, and
Cross domain analysis.
Navs notes Page 40
As an ETL developer you can come across DS Designer and DS Director.
But, some information to be knows about Web console, Information Analyzer, and DS Administrator.
2010DataStage
DAY 14
Description of 7.5.x2 & 8.0.1 Architecture
14.1. Architecture of 7.5.x2:
* Server Components: it is divided into 3 categories, they are
a. Repository
b. Engine
c. Package Installer
Repository: is also called as project or work area.
o Here repository is also Integrated Developer Environment(IDE)
IDE performs design, compile, run, save jobs.
o Repository organize different component at one area is called collection of
components. Some of components are
Job’s
Table definition
Shared container
Routines ….. etc.,
o Repository is for developing application as well as storing application.
Navs notes Page 41
2010DataStage
Engine: it is executing DataStage jobs and it automatically selects the partition
technique.
o Never leave any stage to auto ?
If we leave it auto, it select auto partition technique it causes effect on
the performance.
Package Installer: in this component contains two types of package installer one plug-
in and another is pack’s.
Example: Derivers needed 1100 to install
1100 driver provide
Here, interface is also called as plug-in between computer and printer.
Packs
Best example that normal windows XP acquires Service Pack2 for more capabilities
Here, packs are used to configuration for DataStage to ERP solution.
*Client components: it is divided into 4 categories, they are
a. DS Designer
b. DS Manager
c. DS Director
d. DS Administrator
These categories are shown above what they handle i.e., in page no 39.
Navs notes Page 42
Computer PrinterInterface
ERP SW DS
2010DataStage
14.2. Architecture of 8.0.1:
1. Common user interface: is also called as unified user.
a. Web console
b. Information analyzer
c. DS Designer
d. DS Director
e. DS Administrator
2. Common Repository: is divided into two types
a. Global repository: it is for DataStage jobs files to store here. (it’s checks
security issues)
b. Local repository: it is for individual files stores here(it’s for performance issue)
o common repository is also called as Mata Data SERVER
o three types
Project level MD
Design level MD
Operation level MD
3. Common Engine:
o It is responsible of
Data Profiling analysis
Data Quality analysis
Data Transmission analysis
4. Common Connectivity:
It provides the connections to common repository.
Navs notes Page 43
WC IA DE DI DA
2010DataStage
Common shared services
Table representation of “8.0.1 Architecture”
DAY 15
Enhancement & New feature of version8
In version 8.0.1, there are 8 categories of stages.
Processing stage:
o New Stage:
1. SCD(slow changing Dimension)
2. FTP(File transfer Protocol)
3. WTX(Web Sphere Transfer)
o Enhanced Stage:
1. Surrogate key stage: it is new concept introduced.
2. Lookup stage, previously lookup having
i. Normal lookup
ii. Sparse lookup
Newly added
iii. Range lookup
iv. Case less lookup
Data Base Stage:
o New stages:
IWAY
Navs notes Page 44
REPOSITORY
MD SERVER
Project level MDDesign level MDOperation level MD
DP DQ DT DACommon Engine
Common Connectivity
2010DataStage
Classic federation
ODBC connector
NETEZZA
o Enhanced Stages:
All Stages techniques used with respect to SQL Builder.
Palate of the version 8.0.1
General X
Data Quality new
Data Base
File X
Development & Debug X
Processing
Real time X here, have changes
Restructure X X no changes
o Palate is shortcuts of stages where we can drag n drop into canvas to do design
the job.
o Data Quality is exclusively new concept of 8.0.1.
o Data Base and processing stages have some changes that shown above.
o Other stages are same as version 7.5.x2 i.e., no changes in this version.
Navs notes Page 45
2010DataStage
:: Stages Process & Lab Work::
DAY 16
Starting steps for DataStage tool
The starting of DataStage on the system we must follow the difference steps to do job.
Five difference steps job development process (this is for design a job).
DB2 Repository started and DataStage server started.
After started: select DS Designer
& enter uid:
& enter pwd:
& attach appropriate project:
Select appropriate stage in the palate and dragging them on to the CANVAS.
And link them (or giving connectivity) and after that setting properties is important.
Palate means which contains all stages shortcuts i.e., 7 – stages in 7.5.2 & 8 – stages in 8.0.
This stages are categorized into two groups, they are 1 –> Active Stage (what ever stage is
Navs notes Page 46
Palate -> (it’s from tool bar)
GeneralData QualityDatabaseFileDevelopment & DebugProcessingReal TimeRestructure
Designer Canvas or Editor
CANVAS
Where the place we design the job. Eg: Seq to Seq
SEQ SEQ
admin
**** (eg.: phil)
Project\navs…..
2010DataStage
transmission is called active stage). 2 –> Passive Stage (here what ever stage whether extracting or loading is called passive stage).
In 8 categories we have use sequential stage and parallel stage jobs.
Save, compile and run the job.
Run director (to see views) or to view the status of your job.
DAY 17
My first job creating process
Process:
In computer desktop, the current running process will show at the left Conner in that
a round symbol with green color is to start when it is not automatically starts. i.e.,
whether the server for DataStage was start or not. If not manually to start.
When 8th version of DataStage is installed five client components short cuts visible
on desktop.
Web Console
Information Analyzer
DS Administrator
DS Designer
DS Director
Web Console: when you will click, it displays “ the login page appears”
o If server is not started, it displays “the page cannot open” error will appear.
o If error occurs like that, the server must be restart for doing or creating jobs.
DS Administrator: it is for creating/deleting/organize the project.
Navs notes Page 47
2010DataStage
DS Director: it is for views the status of the job executed, and to view log, status,
warnings.
DS Designer: when you will click on the designer icon, it will display to attach the
project for creating a new job. As shown as below
o User id: admin
o Password: ****
o If authentication failed to login i.e., because repository interface error.
Below figure showing how to authenticate & shows designer canvas for creating
jobs.
After authentication, it displays the Designer canvas
o And it ask which job want to you do, they are
Main frames
Parallel
Sequential
Server jobs
After clicking on parallel jobs, go to tool bar – view – palate.
Navs notes Page 48
Domain
User Name
Password
Project
Attach the project X
Localhost:8080
admin
Teleco
phil
OK
cancel
2010DataStage
In palate the 8 types of stages were displayed for designing a job, they are
General
Data Quality
Data Base
File
Development & Debug
Processing
Real Time
Re – Structure
17.1. File Stage:
Q: How data can read from files?
File stage can read only flat files and the formats of flat files are .txt, .csv, .xml
In .txt there are different types of formats like fwf, sc, csv, s & t, H & T.
.csv means comma separated value.
.xml means extendable markup language.
- In File Stage, there are sub–stages like sequential stage, data set, file set and so on.
o Example how a job can execute:
one sequential file(SF) to another SF.
Source Targeto Source file require target/output properties, ando Target file require input/source properties.
- In source file, how we to read a file?o On double clicking source file, we must set the properties as below
File name \\ browse the file name. Location \\ example in c:\ Format \\ .txt, .csv, .xml Structure \\ meta data
Navs notes Page 49
2010DataStage
General properties of sequential file:
1. Setting / importing source file from local server.
2. Format selection:
- As per input file taken and the data must to be in given format
- Like “tab/ space/ comma” must to be select one them.
3. Column structure defining:
To get the structure of file.
- Steps for load a structure
- Import
o Sequential file
Browse the file and import
Select the import file
o Define the structure.
These three are general properties when we design for simple job.
Navs notes Page 50
Select a file name:
File: \ c:\data\se_source_file.txt File: \? (This option for multiple purposes)
Browse button
C:\data\se_source_file.txt
LOAD
2010DataStage
DAY 18
Sequential File Stage
Sequential file stage also says as “output properties”
- For single structure format only we going to use sequential file stage.
Output Input
Properties Properties
- About Sequential File Stage and how it works:
Step1: Sequential file is file stage, that it to read flat files from different of
extensions(.txt, .csv, .xml)
Step 2: SF it reads/writes sequentially by default, when it reads/writes from single
file.
o And it also reads/writes parallel when it read/writes to or from multiple files
Navs notes Page 51
2010DataStage
Step 3: Sequential stage supports one input (or) one output and one reject link.
Link :
Link is also a stage that transforms data from one stage to another stage.
o That link has divided into categories.
Stream link SF SF
Reject link SF SF
Reference link SF SF
Link Marker:
It is show how the link behaves between the transmissions from source to target.
1. Ready BOX: it is indicate that “a stage is ready with Mata Data” and data transform
between sequential stages to sequential stage.
Ready BOX
2. FAN IN: it indicates when “a data transform from parallel stage to sequential stage” and it
done when collecting happens
FAN IN
3. FAN OUT: it indicates when “a data transform from sequential stage to parallel stage” and
it is also called auto partition.
Navs notes Page 52
2010DataStage
FAN OUT
4. BOX: it indicates when “a data transform from parallel stage to parallel stage” and it is
also known as partitioning.
BOX
5. BOW – TIE: it indicates when “a data transform parallel stage to parallel stage” and it is
also known as re-partitioning.
BOW – TIE
Link Color:
The link color indicates the process in execution of a job.
LINK
RED:
o A link in RED color means
case1: a stage not connected properly and
case2: job aborted
BLACK:
o A link in BLACK color means “a stage is ready”.
Navs notes Page 53
2010DataStage
BLUE:
o A link in BLUE color means “ it indicates that a job execution on process”
GREEN:
o A link in GREEN color means “execution of job finished”.
Compile:
Compile is a translator that source code to target code.
Compiling .C function
HLL BC
ALL *HLL – High Level Language
*ALL – Assembly Level Language
*BC – Binary Code
Compiling process in DataStage:
MC
OSH Code & C++
*MC – Machine Code
Navs notes Page 54
NOTE: “Stage is an operator; operator is a pre – built in component”.
Because the stage that imports import operator for purpose of creating in Native Format.
Native Format is DataStage under stable format. So, stage is a operator.
.C
.OBJ
.EXE
GUI
.OBJ
.EXE
2010DataStage
*OSH – Orchestrate Shell Script
Note: Orchestrate Shell Script generate for all stage except one i.e., Transformer stage that is
done by C++.
In process, it checks for
Link Requirement (checks for link)
Mandatory stage properties
Syntax Rules
DAY 19
Sequential File Stage Properties
Properties:
Read Methods: two options are
o Specific File : user or client to give specifically each file name.
o File Pattern : we can use wild card character and search for pattern i.e., * & ?
For example: C:\eid*.txt
C:\eid??.txt
Reject Mode: to handle a “format/data type/condition” miss match records.
Three options
o Continue : Drops the miss match and continue other records.
o Fail : job aborted.
o Output : its capture the drop data through the link to another sequential file.
First line or record of table: true/false.
o If it false, it display the first line also a drop record.
o Else it is true, it’s doesn’t drop the first record.
Missing File Mode: if any file name miss this option used
Navs notes Page 55
2010DataStage
Two options
o Ok : drops the file name when missed.
o Error : if file name miss it aborts the job.
File Name Column: “source information at the target” it gives information about which
record in which address in local server.
Directly to add a new column to existing table and it’s displays in that column.
Row Number Column: “Source record number at target” it gives information about
which source record number at target table.
It is also directly to add a new column to existing table and it’s displays in that column.
Read First Rows: “will get you top first n-records rows”
o Read First Rows option will asks give n value to display the n number of
records
Filter: “blocking unwanted data based on UNIX filter commands”
Like grep, egrep, ……..so on
Example:
o grep “moon” ; \\ it is case sensitive that display only moon contained records.
o grep - i “moon” \\ it ignores the case sensitive it displays all moon records.
o grep - w “moon” \\ it displays exact match record.
Read from Multiple Nodes: we can read the data parallel from using sequential stage
Reads parallel is possible
Loading parallel is not possible
LIMITATIONS of SF:
o It should be sequential processing( process the data in sequential)
o Memory limit 2gb(.txt format)
Navs notes Page 56
2010DataStage
o Problem with sequential is conversions.
Like ASCII – NF – ASCII – NF
o It is lands or resides the data “outside of boundary” of DataStage.
DAY 20
General settings DataStage and about Data Set
Default setting for startup with parallel job:
- Tools
o Options
Select a default
And to create new: it ask which type of job u want.
- Types of jobs are main frames/parallel/sequential/server.
- After setting above when we restart the DS Designer it directly goes designer canvas.
According naming standards every stage has to be named.
o Naming a stage is simple, just right click on a stage rename option is visible
and name a stage as naming standards.
General Stage:
In this stage the some of stage were used for commenting a stage what they behave or
what a stage can perform to do i.e., simple giving comments for a stage.
Navs notes Page 57
2010DataStage
Let we discuss on Annotation & Description Annotation
- Annotation: it is for stage comment.
- Description Annotation: it is used for job title (any one tile can keep).
Parallel Capable of 3 jobs:
Resides into or
SRC TRG
Extracting landing the data into LS/RR/db
Q: In which format the data sends between the source file to target file?
A: if we send a .txt file from source, it is ASCII format because .txt file support only ASCII
format and DataStage support the Native format only, here the ASCII code will convert into
Native format that is understandable to DataStage. And at target ASCII code will convert
into .txt format to user/client visible.
“Native Format” is also called as Virtual Dataset.
NF
ASCII ASCII
src_f.txt trg_f.txt
Navs notes Page 58
When we convert ASCII code into NF. SRC need to import an operator.
When we convert NF code into ASCII. Target need to import an operator.
2010DataStage
Data Set (DS):
“It is file stage, and it is used staging the data when we design dependent jobs”.
Data Set over comes the limitation of sequential file stage for the better performance.
By default Data Set sends the data in parallel.
In Data Set the data lands in to “Native Format”.
Q: How the Data Set over comes the sequential file limitation?
- By default the data process parallel.
- More than 2 GB.
- No need of conversion, because Dataset represent or data directly resides into Native
format.
- The data Lands in the DataStage repository.
- Data Set extension is *.ds
Structure saving as “st_trg”
src_f.txt trg_f.ds
Q: How the conversion is easy in Data Set?
- we can copy the “trg_f.ds” file name and also we must save the structure of the
trg_f.ds example st_trg.
- We can use the saved file name and structure of the target in other job.
copying the structure st_trg
& trg_f.ds for reusing here.
trg_f.ds trg_f.txt
Navs notes Page 59
2010DataStage
- Data Set can read only Native Format file, like DataStage reads only orchestrate
format.
DAY 21
Types of Data Set (DS)
Two types of Data Set, they are
Virtual (temporary)
Persistency (permanent)
- Virtual : it is a Data Set stage that the data moves in the link from one stage to another
stage i.e., link holds the data temporary.
- Persistency : means the data sending from the link it directly lands into the repository.
That data is permanent.
Alias of Data Set:
o ORCHESTRATE FILE
o OS FILE
Q: How many files are created internally when we created data set?
Navs notes Page 60
2010DataStage
A: Data Set is not a single file; it creates multiple files when it created internally.
o Descriptor file
o Data file
o Control file
o Header file
Descriptor File : it contains schema details and address of data.
Data File: consists of data in the Native Format and resides in DataStage repository.
Control File :
It resides in the operating system and both acting as interface
between descriptor file and data file.
Header File :
Physical file means it stores in the local drive/ local server.
Permanently stores in the install program files c:\ibm\inser..\server\dataset{“pools”}
Q: How can we organize Data Set to view/copy/delete in real time and etc.,
A: Case1: we can’t directly delete the Data Set
Case2: we can’t directly see it or view it.
Data Set organizes using utilities.
o Using GUI i.e., we have utility in tool (dataset management)
o Using Command Line: we have to start with $orachadmin grep “moon”;
Navigation of organize Data Set in GUI:
o Tools
Dataset Management
- File_name.ds(eg.: dataset.ds)
o Then we will see the general information of dataset
Schema window
Data window
Navs notes Page 61
2010DataStage
Copy window
Delete window
At command line
o $orachadmin rm dataset.ds (this is correct process) \\ this command for remove
a file
o $rm dataset.ds (this is wrong process) \\ cannot write like this
o $ds records \\ to view files in a folder
Q: What is the operator which associates to Dataset:
A: Dataset doesn’t have any operator, but it uses copy operator has a it’s operator.
Dataset Version:
- Dataset have version control
- Dataset has version for different DataStage version
- Default version in 8 is it saves in the version 4.1 i.e., v41
Q: how to perform version control in run time?
A: we have set the environment variable for this question.
Navigation for how to set a environment variable.
Job properties
o Parameters
Add environments variable
- Compile
o Dataset version ($APT_WRITE_DS_VERSION)
Click on that.
Navs notes Page 62
2010DataStage
After doing this when we want to save the job, it will ask whether which version you
want.
DAY 22
File Set & Sequential File (SF) input properties
File Set (FS): “It is also a staging the data”.
- File stage is same to design in dependent jobs.
- Data Set & File Set are same, but having minor differences
- The differences between DS & FS are shown below
Navs notes Page 63
Data Set File Set
Having parallel extendable capabilities
More than 2 GB limit
NO REJECT link with the Dataset
DS is exclusively for internal use DataStage environment
Copy (file name) operator
Native format
.ds files saves extension
Multiple segments
Having parallel extendable capabilities
More than 2GB limit
REJECT LINK with in File Set
External application create FS we use the any other application
Import / Export operator
Binary Format
.fs extension
Multiple files
2010DataStage
But, Data Set have more performance than File Set.
Sequential File Stage : input properties
- Setting input properties at target file, and at target there have four properties
1. File update mode
2. Cleanup on failure
3. First line in column names
4. Reject Mode
File Update Mode: having three options – append/create (error if exists)/overwrite
o Append: when the multiple file or single file sending to sequential target it’s
appends one file after another file to single file.
o Create (error if exists): just creating a file if not exist or given wrong.
o Overwrite: it’s overwriting one file with another file.
Setting passing value in Run time(for file update mode)
o Job properties
Parameters
- Add environment variables
o Parallel
Automatically overwrite
($APT_CLOBBER_OUTPUT)
Cleanup on Failure: having two options – true/false,
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2010DataStage
True – the cleanup on failure option when it is true it adds partially coded or records.
Its works only when “file update mode” is equal to append.
False – it’s simple appends or overwrites the records.
First Line in Column Names: having two options – true/false.
True – it is enable the first row or record as a fields of column
False – it is simple reads every row include first row read as record.
Reject mode: here reject mode is same like as output properties we discussed already before.
In this we have three options – continue/fail/output.
Continue – it just drops when the format/condition/data type miss match the data and
continues process remain records.
Fail – it just abort the file when format/condition/data type miss match were found.
Output – it capture the drops record data.
DAY 23
Development & Debug Stage
The development and debug stage having three categories, they are
1. Stage that Generated Data:
a. Row Generated Data
b. Column Generated Data
2. The stage that used to Pick Sample Data:
a. Head
b. Tail
c. Simple
3. The stage that helps in Debugging:
Navs notes Page 65
2010DataStage
a. Peek
Simply say in development and debug we having 6 types of stages and the 6 stages
where divided into three categories as above shown.
23.1. Stages that Generated Data:
Row Generator Data: “It having only one output”
- The row generator is for generating the sample data; in some cases it is used.
- Some cases are,
o When client unable to give the data.
o For doing testing purpose.
o To make job design simple that shoots for jobs.
- Row Generator can generate the junk data automatically by considering data type, or
we manual can set a some related understandable data by giving user define values.
- In this having only one property and select a structure for creating junk data.
Row Generator design as below:
ROW Generator DS_TRG
Navigation for Row Generator:
- Opening the RG properties
- Properties
o Number of records = XXX( user define value)
- Column
o Load structure or Meta data if existing or we can type their.
For example n=30
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2010DataStage
- Data generated for the 30 records and the junk data also generated considering the data
type.
Q: how to generate User define value instead of junk data?
A: first we must go to the RG properties
- Column
o Double click serial number or press ctrl+E
Generator
Type = cycle/random (it is integer data type)
In integer data type we have three option
Under cycle type:
There are three types of cycle generated data Increment, Initial value, and limit.
Q: when we select initial value=30?
A: it starts from 30 only.
Q: when we select increment=45?
A: it going to generate a cycle value of from 45 and after adds every number with 45.
Q: when we select limit=20?
A: it is going to generate up to limit number in a cycle form.
Under Random type:
There are three types of random generated data – limit, seed, and signed.
Q: when we select limit=20?
A: it going to generate random value up to limit=20 and continues if more than 20 rows.
Q: when we select seed=XX;
A: it is going to generate the junk data for random values.
Navs notes Page 67
2010DataStage
Q: when we select signed?
A: it going to generate signed values for the field (values between –limit and +limit),
otherwise generate values between 0 and +limit.
Column Generator Data: “it having the one input and one output”
- Main purpose of column generator to group a table as one.
- And by using this we add extra column for the added column the junk data will be
generated in the output.
- Here mapping should be done in the column generated properties, means just drag and
dropping created column into existing table.
Sequential file Column Generator DataSet
- Coming to the column generator properties.
- To open the properties just double clicking on that.
Navigation:
- Stage
o Options
Column to generate =?
And so on we can give up to required.
- Output
o Mapping
After adding extra column it will visible here, and for mapping we drag
simple to existing table into right side of a table.
- Column
o We can change data type as you require.
In the output,
- The junk data will generate automatically for extra added columns.
Navs notes Page 68
2010DataStage
- For manual we can generate some meaning full data to extra column’s
- Navigation for manual:
o Column
Ctrl+E
Generator
o Algorithm = two options “cycle/ alphabet”
o Cycle – it have only one option i.e., value
o Alphabet – it also have only one option i.e., string.
- Cycle is same like above shown in row generator.
Q: when we select alphabet where string=naveen?
A: it going to generate different rows with given alphabetical wise.
DAY 24
Pick sample Data & Peek
24.1. Pick sample data: “it is a debug stage; there are three types of pick sample data”.
- Head
- Tail
- Sample
Head : “it reads the top ‘n’ records of the every partition”.
o It having one input and one output.
o In the head stage mapping must and should do.
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2010DataStage
SF_SRC HEAD DS_TRG
Properties of Head:
o Rows
All Rows(after skip)=false
- It is to copy all rows to the output following any requested skip
positioning
Number of rows(per partition)=XX
- It copy number of rows from input to output per partition.
o Partitions
All partition = true
- True: copies row from all partitions
- False: copies from specific partition numbers, which must be
specified.
Tail: “it is debug stage, that it can read bottom ‘n’ rows from every partition”
o Tail stage having one input and one output.
o In this stage mapping must and should do. That mapping done in the tail output
properties.
SF_SRC TAIL_F DS_TRG
Properties of Tail:
o The properties of head and tail are similar way as show above.
o Mainly we must give the value for “number of rows to display”
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2010DataStage
Sample: “it is also a debug stage consists of period and percentage”
o Period: means when it’s operating is supports one input and one output.
o Percentage: means when it’s operating is supports one input and multiple of
outputs.
SF_SRC SAMPLE DS_TRG
Period: if I have some records in source table and when we give ‘n’ number of
period value it displays or retrieves the every nth record from the source table.
Skip: it also displays or retrieves the every nth record from given source table.
Percentage: it reads from one input to multiple outputs.
o Coming to the properties
Options
- Percentage = 25 and we must set target =1
- Percentage = 50 , target = 0
- Percentage = 15 , target = 2
o Here we setting target number that is called link order.
o Link Order: it specifies to which output the specific data has to be send.
o Mapping: it should be done for multiple outputs.
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Target1
Target2
SF_SRC SAMPLE
Target3
NOTE: sum of percentage of all outputs must be less than are equal to ‘<=’ to ‘n’ records of
input records.
o In the percentage it distributes the data in percentage form. When sample
receives the 90% of data from source. It considers 90% as 100% and it
distributes as we specify.
24.2. PEEK: “it is a debug stage and it helps in debugging stage”
SF_SRC PEEK
It is used in three types they are
1. It can use as copying the data from Source to multiple outputs.
2. Send the data into logs.
3. And it can use as stub stage.
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2010DataStage
Q: How to send the data into logs?
Opening properties of peek stage, we must assign
o Number of row = value?
o Peek record output mode = job log and so on, as per options
o If we put column name = false, it doesn’t shows the column in the log.
For seeing the log records that we stored.
o In DS Director
From Peek – log – peek - We see here ‘n’ values of records and fields
Q: When the peek act as copy stage?
A: It is done when the sequence file it doesn’t send the data to multiple outputs. In that time
the peek act as copy stage.
Q: What is Stub Stage?
A: Stub Stage is a place holder, because in some situations a client requires only dropped data.
In that time the stub stage acts as a place holder which holds the output data as temporary,
and its sends the rejected data to the another file.
DAY 25
Database Stages
In this stage we have use generally oracle enterprise, ODBC enterprise, Tara data with ODBC,
and dynamic RDBMS and so on.
25.1. Oracle Enterprise:
“Oracle enterprise is a data base stage, it reads tables from the oracle data base
from source to the target”
o Oracle enterprise reads multiple tables from, but it loads in the one output.
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Oracle Enterprise Data Set
o Properties of Oracle Enterprise(OE):
Read Method have four options
Auto Generated \\ it generated auto query
SQL Builder \\ its new concept apart comparing from v7 to v8.
Table \\ giving table name here
User Defined \\ here we are giving user defined SQL query.
If we select table option
Table = “<table name>”
Connection
Password = *****
User = Scott
Remote server = oracle
o Navigations for how the data load to the column
This is for already data present in plug-in.
Select load option in column
Going to the table definitions
Than to plug-in
Loading EMP table from their.
If table not in the not their in plug-in.
Select load option in column
Then we go to import
Import “meta data definition”
o Select related plug-in
Navs notes Page 74
2010DataStage
Oracle
User id: Scott
Password: tiger
After loading select specific table and import.
After importing into column, in define we must change hired
date data type as “Time Stamp”.
Q: A table containing 300 records in that, I need only 100 fields from that?
A: In read method we use user-defined SQL query to solve this problem by writing a query for
reading 100 records.
But by the first read method option, we can auto generate the query by that we can use
by coping the query statement in user-defined SQL.
Q: What we can do when we don’t know how to write a select command?
A: Selecting in read method = SQL Builder
After selecting SQL Builder option from read method
o Oracle 10g
o From their dragging which table you want
o And select column or double clicking in the dragged table
There we can select what condition we need to get.
It is totally automated.
NOTE: in version 7.5.x2 we don’t have saving and reusing the properties.
Data connection: its main purpose is reusing the saved properties.
Q: How to reuse the saved properties?
A: navigation for how to save and reuse the properties
Opening the OE properties
o Select stage
Data connection
Navs notes Page 75
2010DataStage
There load saved dc
o Naveen_dbc \\ it is a saved dc
o Save in table definition.
DAY 26
ODBC Enterprise
ODBC Enterprise is a data base stage
About ODBC Enterprise:
Oracle needs some plug-ins to connect the DataStage. When DataStage version7
released that time the oracle 9i provides some drivers to use.
When coming to connection oracle enterprise connects directly to oracle data base. But
ODBC needs OS drivers to hit oracle or to connect oracle data base.
Navs notes Page 76
ODBC
Enterprise
ORACLE DB
OS
Oracle Enterprise
2010DataStage
Directly hitting
Use OS drivers to hit the oracle db
Difference between Oracle Enterprise (OE) and ODBC Enterprise
Q: How database connect using ODBC?
ODBCE Data Set
First step: opening the properties of ODBCE
Read method = table
o Table = EMP
Connection
Navs notes Page 77
OE ODBCE
Version dependent
Good performance
Specific to oracle
Uses plug-ins
No rejects at source
Version independent
Poor performance
For multiple db
Uses OS drivers
Reject at SRC &TRG.
2010DataStage
o Data Source = WHR \\ WHR means name of ODBC driver
o Password = ******
o User = Scott
Creating of WHR ODBC driver at OS level.
o Administration tools
ODBC
Add
o MS ODBC for Oracle
Giving name as WHR
Providing user name= Scott
And server= tiger.
ODBCE driver at OS level having lengthy process to connect, to over this ODBC
connector were introduced.
Using ODBC Connector is quick process as we compare with ODBCE.
Best Feature by using ODBC Connector is “Schema reconciliation”. That
automatically handles data type miss match between the source data types and
DataStage data types.
Differences between ODBCE and ODBC Connector.
Navs notes Page 78
ODBCE ODBC Connector
It cannot make the list of Data Source Name (DSN).
In the ODBCE “no testing the connection”.
ODBCE read sequentially and load parallel (poor performance).
It provides the list have in ODBC DSN.
In this we can test the connection by test button.
It read parallel and loads parallel (good performance).
2010DataStage
Properties of ODBC Connector:
o Selecting Data Source Name DSN = WHR
o User name = Scott
o Password = *****
o SQL query
26.1. MS Excel with ODBCE:
First step is to create MS Excel that is called “work book”. It’s having ‘n’ number of
sheets in that.
For example CUST work book is created
Q: How to read Excel work book with ODBCE?
A: opening the properties of ODBCE
Read method = table
o Table = “empl$” \\ when we reading from excel name must be in double codes
end with $ symbol.
Connections
o DSN = EXE
o Password = *****
o User = xxxxx
Column
o Load
Import ODBC table definitions
DSN \\ here select work book
User id & password
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2010DataStage
o Filter \\ enable by click on include system tables
o And select which you need & ok
In Operating System
o Add in ODBC
MS EXCEL drivers
Name = EXE \\ it is DSN
Q: How do you read Excel format in Sequential File?
A: By changing the CUST excel format into CUST.csv
26.2. Tara Data with ODBCE:
Tara Data is like an oracle cooperation data base, which use as a data base.
Q: How to read Tara Data with ODBC
A: we must start the Tara Data connection (by clicking shortcut).
o And in OS also we must start
Start ->control panel ->Administrator tools -> services ->
Tara Data db initiator \\ must start here
o Add DSN in ODBC drivers
Select Tara data in add list
We must provide details as shown below
User id = tduser
Password = tduser
Server : 127.0.0.1
After these things we must open the properties of ODBCE
o Read method = table
Table = financial.customer
o Connections
Navs notes Page 80
2010DataStage
DSN = tduser
Uid = tduser
Pwd = tduser
Column
o Load
Import
Table definitions\plug-in\taradata
Server: 127.0.0.1
Uid = tduser
Pwd = tduser
After all this navigation at last we view the data, which we have load in source.
DAY 27
Dynamic RDBMS and PROCESSING STAGE
27.1. Dynamic RDBMS:
“It is data base stage; it is also called as DRS”
It supports multiple inputs and multiple outputs
Navs notes Page 81
2010DataStage
Ln_EMP_Data Data Set
DRS
Ln_DEPT_Data
Data Set
It all most common properties of oracle enterprise.
Coming to DRS properties
o Select db type i.e., oracle
o Oracle
Scott \\ for authentication
Tiger
o At output
Ln_EMP_Data \\ set emp table here
And Ln_DEPT_Data \\ set dept table here
o Column
Load
Meta data for table EMP & DEPT.
In oracle enterprise we can read multiple files, but we can’t load into multiple files.
We can solve this problem with DRS that we can read multiple files and load in to
multiple files.
Some of data base stages:
IWay can use in source only to set in output properties.
Navs notes Page 82
2010DataStage
Netezza can use in target only to set in input properties.
27.2. Processing Stage:
In this 28 processing stages are there, but we use 10 stages generally. And the 10
stages are very important.
They are,
1. Transformer
2. Look UP
3. Join
4. Copy
5. Funnel
6. Remove duplicates
7. Slowly changing dimension
8. Modify
9. Sort
10. Surrogate key
27.3. Transformer Stage:
The symbol of Transformer Stage is
A simple query that we solving by using transformer i.e,
Q: calculate the salary and commission of an employee from EMP table.
Navs notes Page 83
2010DataStage
Oracle Enterprise Transformer Data Set
Here, setting the connection here, source field and structure available
and load Meta data in to column mapping should be do.
Transformer Stage is “all in one
stage”.
Properties of Transformer Stage:
o For above question we must create a column to write description
In the down at output properties clicking in empty position.
That column we name as NETSAL
By double clicking on the NETSAL, we can write derivation here.
For example, IN.SAL + IN.COMM \\ we can write by write clicking
their
It visible in input column\function\ and so on.
After that when we execute the null values records it drops and remaining records it
sends to the target.
o For this we can functions in derivation
IN.SAL + NullToZero (IN.COMM)
o By this derivation we can null values records as target.
Q: NETSAL= SAL + COMM
Logic: if NETSAL > 2000 then TakeHome = NETSAL – 200 else TakeHome = NETSAL
+200; how to include this logic in derivation?
Navs notes Page 84
2010DataStage
A: adding THome column in output properties.
In THome derivation part we include this logic
o If (IN.SAL + NullToZero (IN.COMM))> 2000
Then (IN.SAL + NullToZero (IN.COMM)) – 200
Else (IN.SAL + NullToZero (IN.COMM) ) + 200
o By this logic it takes more time in huge records, so the best way to over this
problem is Stage Variable.
Stage Variable: “it is a temporary variable which will holds the value until the process
completes and which doesn’t sent to the result to output”
Stage variable is shown in the tool bar of transformer properties.
After clicking that it visible in the input properties
In stage variable we must add a column for example, NS
Variables to adding column
1 NS 0 integer - 4 0
After adding NS column
To NS column including the derivation, IN.SAL + NullToZero (IN.COMM).
Adding these derivations to the input properties to created columns.
o NETSAL = NS
o THome = if (NS > 2000) then (NS -200) else (NS + 200).
DAY 28
Transformer Functions-I
Examples on Transformer Functions:
1. Left Function
2. Right Function
3. Substring Function
4. Concatenate Function
5. Field Function
Navs notes Page 85
2010DataStage
6. Constraints Function (Filter)
For example, a word MINDQUEST, from that word we need only QUE.
Right Function using the above for question - R(L(7),3)
Left Function – L(R(5),3)
Substring – SST(5,3)
Filter: DataStage in 3 different ways
1. Source level
2. Stages (filter, switch, extended filter)
3. Constraints (transformer, lookup)
Constraints:
“In transformer constraints used as filter, means constraints is also called as filter”
Q: how a constraint used in Transformer?
A: in transformer properties, we will see a constraints row in output link. There we can write
the derivation by double clicking.
Differences between Basic transformer and parallel transformer:
Navs notes Page 86
Basic Transformer Parallel Transformer
Its effects on performance.
Basic Tx can only execute up to SMP.
Basic Tx can call the Routines which is in basic and shell scripting language.
Don’t effects on performance, but it effects on compile time.
Can execute in any platform.
It supports wide range of language or multiple languages or all languages.
2010DataStage
NOTE: Tx is very sensitive with respect to Data Types, if an source and target be cannot
different data types.
Q: How the below file can read and perform operation like filtering, separating by using left,
right, substring functions and date display like DD-MM-YYYY?
A: File.txt
Design:
IN1 IN2
SF Tx1 Tx2
IN3
Navs notes Page 87
HINVC23409CID45432120080203DOLTPID5650 5 8261.99TPID5655 4 2861.69TPID5657 7 6218.96HINVC12304CID46762120080304EUOTPID5640 3 5234.00TPID5645 2 7855.67TPID5657 9 7452.28HINVC43205CID67632120080405EUOTPID5630 8 1657.57TPID5635 6 9564.13TPID5637 1 2343.64
2010DataStage
OUT
Tx3 DS
Total five steps to need to solve the given question:
Step 1: Loading file.txt into sequential file, in the properties of sequential file loading the
whole data into one record. Means here creating one column called REC and no need of
loading of Meta data for this.
Step 2: IN1 Tx- Properties, in this step we are filtering the “H” staring records from the given
file. Here, we are creating two columns TYPE and DATA.
Step 3: IN2 Tx properties, here creating four column and separating the data as per created
columns.
Navs notes Page 88
REC
IN1 CONSTRAINT Left (IN1.REC,1)=”H”
Left (IN1.REC, 1) TYPE
IN1.REC DATA
IN1
IN2
Derivation Column Name
TYPEDATA
Left (IN1.REC, 1) INVCNOLeft (Right (IN2.DATA, 21), 9) CIDIN2.DATA [20, 8] BILL_DATERight (IN2.DATA, 4) CURR
IN2 IN3
Derivation Column Name
2010DataStage
Step 4: IN3 Tx properties, here BILL_DATE column going to change into DD-MM-YYYY
format using Stage Variable.
Step 5: here, setting the output file name for displaying the BILL_DATE.
DAY 29
Transformer Functions-II
Examples on Transformer Functions II:
1. Field Function : “it separates the fields using delimiter support”.
2. Trim : “it removes all special characters”.
3. Trim B : “it removes all after spaces”.
4. Trim F : “it removes all before spaces”.
Navs notes Page 89
INVCNOCIDBILL_DATECURR
IN3.INVCNO INVCNOIN3.CID CIDD:’-‘: M:’-‘: Y BILL_DATEIN3.CURR CURR
IN3
OUT
Derivation Column Name
Right (IN3.BILL_DATE, 2) DRight (Left (IN3.BILL_DATE, 6), 2) MLeft (IN3.BILL_DATE, 4) Y
Stage Variable
Derivation Column Name
2010DataStage
5. Trim T & L : “it removes all after and before spaces”.
6. Strip White Spaces : “it removes all spaces”.
7. Compact White Spaces : “it removes before, after, middle one, spaces”.
Q: A file.txt consisting of special character, comma delimiters and spaces (before, after, and in
between). How to solve by above functions and at last it to be one record?
File.txt
Design:
IN1 IN2
SF Tx Tx
IN3
OUT
Navs notes Page 90
EID,ENAME,STATE
111, NaVeen, AP
222@, MUnNA, TN
@333, Sra van, KN@
444, @ San DeeP, KN
555, anvesh,MH
2010DataStage
Tx DS
Total Five steps to solve the File.txt using above functions:
Step 1: Here, extracting the file.txt and setting into all data into one record to the new column
created that REC. no need of load meta data to this.
Point to remember keep that first line is column name = true.
Step 2:IN1. Tx properties
In link IN1 having the REC, that REC to divide into fields by comma delimiter i.e.,
using field functions.
Step 3: IN2. Tx properties
Here, to remove special characters, spaces, lower cases into upper cases by using the
trim, Strip Whitespaces (SWS), Up case functions.
Step 4: IN3. Tx properties
Navs notes Page 91
RECField(IN2.REC,’,’,1) EIDField(IN2.REC,’,’,2) ENAMEField(IN2.REC,’,’,3) STATE
IN1 IN2
Derivation Column Name
EIDENAMESTATE Trim(IN2.EID,”@”,””) EID
Upcase(Trim(SWS(IN2.ENAME,”@”,””)) ENAMETrim(SWS(IN2.EID),”@”,””) STATE
IN2 IN3
Derivation Column Name
2010DataStage
Here, all rows that divided into fields are concatenating means adding all records into
one REC.
Step 5:
For the output, here assigning a target file. And at last the answer will display in one
record but all special characters, spaces were removed after doing are implementing
the transformer functions to the above file.txt.
Final output:
Trg_file.ds
29.1. Re-Structure Stage:
1. Column Export
2. Column Import
Column Export:
“it is used to combine the multiple of columns into single column” and it is also like
concatenate in the transformer function.
Properties:
o Input
Column method = explicit
Navs notes Page 92
EIDENAMESTATE IN3.EID: IN3.ENAME: IN3.STATE REC
IN3 OUT
Derivation Column Name
REC
111NAVEEN AP222 MUNNATN333SRAVAN KN444SAN DEEPKN555 ANVESHMH
2010DataStage
Column To Export = EID
Column To Export = ENAME
Column To Export = STATE
o Output
Export column type = “varchar”
Export output column = REC
Column Import:
“it is used to explore from single column into multiple columns” and it is also like field
separator in the transformer function.
Properties:
o Input
Column method=
Column To Import = REC
o Output
Import column type = “varchar”
Import output column= EID
Import output column= ENAME
Import output column= STATE
DAY 30
JOB Parameters (Dynamic Binding)
Dynamic Binding:
“After compiling the job and passing the values during the runtime is known as
dynamic binding”.
Assuming one scenario that when we taking a oracle enterprise, we must provide the
table and load its meta data. Here table name must be static bind.
Navs notes Page 93
2010DataStage
But there is no need for giving the authentication to oracle are to be static bind,
because of some security reasons. For this we can use job parameters that can provide
values at runtime to authenticate.
Job parameters:
“job parameters is a technique that passing values at the runtime, it is also called
dynamic binding”.
Job parameters are divided into two types, they are
o Local variables
o Global Variable
Local variables (params): “it is created by the DS Designer only, it can use with in the
job only”.
Global Variables : “it is also called as environment variables”, it is divided into two
types. They are,
o Existing: comes with in DataStage, in this two types one general and another
one parallel. Under parallel compiler, operator specific, reporting will
available.
o User Defining : it is created in the DataStage administrator only.
Q: How to give Runtime values using parameters for the following list?
a. To give runtime values for user ID, password, and remote server?
b. Department number (DNO) to keep as constraint and runtime to select list of any
number to display it?
c. Add BONUS to SAL + COMM at runtime?
d. Providing target file name at runtime?
e. Re-using the global and parameter set?
Navs notes Page 94
NOTE: “The local parameters that created one job they cannot be reused in other job, this is up to version7. But coming to version8 we can reuse them by technique called parameter set”. But in version7 we can also reuse parameters by User Define values by DataStage Administrator.
2010DataStage
Design:
ORACLE Tx Data Set
Step1:
“Creating job parameters for given question in local variable”.
Job parameters
o Parameters
Name DNAME Type Default value
UID USER string SCOTT
PWD Password Encrypted ******
RS SERVER String ORACLE
DNO DEPT List 10
BONUS BONUS Integer 1000
IP DRIVE String C:\
FOLDER FOLDER String Repository\
TRG FILE TARGET String dataset.ds
Here, a, b, c, d are represents a solution for the given question.
Step 2:“Creating global job parameters and parameter set”.
DS Administrator
o Select a project
Properties
General
o Environment variables
User defined (there we can write parameters)
Name DNAME Type Default value
Navs notes Page 95
a
b
c
d
2010DataStage
UID USER string SCOTT
PWD Password Encrypted ******
RS SERVER String ORACLE
Here, global parameters are preceded by $ symbol.
For Re-use, we must
o Add environment variables
User defined
UID $UID
PWD $PWD
RS $RS
Step 3:
“Creating parameter set for multiple values & providing UID and PWD other values
for DEV, PRD, and TEST”.
In local variables job parameters
o Select multiple of values by clicking on
And create parameter set
Providing name to the set
o SUN_ORA
Saving in Table definition
In table definition
o Edit SUN_ORA values to add
Name UID PWD SERVER
DEV SYSTEM ****** MOON
PRD PRD ****** SUN
TEST TEST ****** ORACLE
For re-using this to another job.
o Add parameters set (in job parameters)
Table definitions
Navs
Navs notes Page 96
2010DataStage
o SUN_ORA(select here to use)
NOTE: “Parameter set use in the jobs with in the project only”.
Step 4:
“In oracle enterprise properties selecting the table name and later assign created job
parameter as shown below”.
Properties:
Read method = table
o Table = EMP
Connection
o Password = #PWD#
o User = #UID#
o Remote Server = #RS#
Column:
Load
o Meta data for EMP table
Step 5:
“In Tx properties dept no using as a constraint and assign bonus to bonus column”.
Navs notes Page 97
Insert job parameters
$UID
$PWD global environment variables
$RS
SUN_ORA.UID
SUN_ORA.PWD parameter set
SUN_ORA.RS
UID
PWD Local variables
RS
Parameters
EIDENAMESTATESALCOMMDEPTNO
ININ.SAL + NullToZero(IN.COMM) NS
Stage Variable
Derivation Column Name
2010DataStage
Here, DNO and BONUS are the job parameters we have created above to use here.
For that simply right click->job parameters->DNO/BONUS (choose what you want)
Step 6:
“Target file set at runtime, means following below steps to follow to keep at runtime”.
Data set properties
o Target file= #IP##FOLDER##TRGFILE#
Here, when run the job it asks in what drive, and in which folder. At last it asks what target
file name you want.
DAY 31
Sort Stage (Processing Stage)
Q: What is sorting?
“Here sorting means higher than we know actually”.
Q: Why to sort the data?
Navs notes Page 98
IN.EID EIDIN.ENAME ENAMENS NETSALNS+BONUS BONUS
OUT
Derivation Column Name
Constraint: IN.DEPTNO = DNO
2010DataStage
“To provide sorted data to some sort stages like join/ aggregator/ merge/ remove
duplicates for the good performance”.
Two types of sorting:
1. Traditional sorting : “simple sort arranging the data in ascending order or descending
order”.
2. Complex sorting : “it is only for sort stages and to create group id, blocking unwanted
sorting, and group wise sorting”.
In DataStage we can perform sorting in three levels:
Source level: “it can only possible in data base”.
Link level: “it can use in traditional sort”.
Stage level: “it can use in traditional sorting as well as complex sorting”.
Q: What is best level to sort when we consider the performance?
“At Link level sort is the best we can perform”.
Source level sort:
o It can be done in only data base, like oracle enterprise and so on.
o How it will be done in Oracle Enterprise (OE)?
Go to OE properties
Select user define SQL
o Query: select * from EMP order by DEPTNO.
Link level sort:
o Here sorting will be done in the link stage that is shown how in pictorial way.
o And it will use in traditional sorting only.
o Link sort is best sort in case of performance.
Navs notes Page 99
2010DataStage
OE
JOIN DS
Q: How to perform a Link Sort?
“Here as per above design, open the JOIN properties”.
And go to partitions
o Select partition technique (here default is ‘auto’)
Mark “perform sort”
When we select unique (it removes duplicates)
When we select stable (it displays the stable data)
Q: Get all unique records to target1 and remaining to another target2?
“For this we must create group id, it indicates the group identification”.
It is done in a stage called sort stage, in the properties of the sort stage and in the
options by keeping create key change column (CKCC) = “true”, default is false.
Here we must select to which column group id you want.
Sort Stage:
“It is a processing stage, that it can sort the data in traditional sort or in complex sort”.
Sort Stage
Navs notes Page 100
2010DataStage
Complex sort means to create group id, blocking unwanted sorting, and group wise
sorting in some sort stage like join, merge, aggregate, and remove duplicates.
Traditional sort means sorting in ascending order or descending order.
Sort Properties:
Input properties
o Sorting key = EID (select the column from source table)
o Key mode = sort (sort/ don’t sort (previously sorted)/ don’t sort (previously
grouped))
o Options
Create cluster key change column = false (true/ false)
Create key change column = (true/ false)
True = enables group id.
False = disables the group id.
Output properties
o Mapping should be done here.
DAY 32
A Transformer & Sort stage job
Q: Sort the given file and extract the all addresses to one column of a unique record and count
of the addresses to new column.
Navs notes Page 101
2010DataStage
File.txt
Design:
SF Sort1 DS
Tx Sort2
Sequential File (SF): here reads the file.txt for the process.
Sort1: here sorting key = EID
And enables the CKCC for group id.
Transformer (TX): here logic to implement operation for target.
o Properties of TX :
Navs notes Page 102
EID, ENAME, ACCTYPE
111, munna, savings333, naveen, loans222, kumar, credit111,munna, current222, kumar, loans111, munna, insurance333, naveen, current111, munna, loans222, kumar, savings
EIDENAMEACCTYPEKeyChange
IN2
OUTDerivation Column Name
if (IN2.keychange = 1) then IN2.ACCTYPE func1else func1 :’,’: IN2.ACCTYPE
if(IN2.keychange=1) then 1 else c+1 c
Stage Variable
Derivation Column Name
2010DataStage
For this logic output will displays like below:
Sort2:
o Here, in the properties we must set as below.
Stage
Key=ACCTYPE
o Sort key mode = sort
o Sort order = Descending order
Navs notes Page 103
IN2.EID EIDIN3.ENAME ENAMEfunc1 ACCTYPEc COUNT
EID, ENAME, ACCTYPE COUNT
111, munna, savings 1111,munna, savings, current 2111, munna, savings, current, insurance 3111, munna, savings, current, insurance, loans 4222, kumar, credit 1222, kumar, credit ,loans 2222, kumar, credit ,loans, savings 3333, naveen, current 1333, naveen, current, loans 2
2010DataStage
Input
Partition type: hash
Sorting
o Perform sort
Stable (uncheck)
Unique (check this)
o Selected
Key= count
Usage= sorting, partitioning
Options= ascending, case sensitive
Output
Mapping should be doing here.
Data Set (DS):
o Input:
partition type: hash
o Sorting:
Perform sort
Stable (check this)
Unique (check this) Final output:
o Selected
Key= EID
Usage= sorting, partition
Ascending
DAY 33
FILTER STAGE
Filter means “blocking the unwanted data”. In DataStage Filter stage can perform in three
level, they are
1. Source level
Navs notes Page 104
EID, ENAME, ACCTYPE, COUNT
111, munna, sav, curr, insu, loans 4222, kumar, credit ,loans, sav 3333, naveen, current, loans 2
2010DataStage
2. Stage level
3. Constraints
Source Level Filter : “it can be done in data base and as well as in file at source level”.
o Data Base: by write filter quires like “select * from EMP where DEPTNO =
10”.
o Source File: here we have option called filter there we can write filter
commands like “grep “moon”/ grep –I “moon”/ grep –w “moon” ”.
Stage Filter :
o “Stage filters use in three stages, and they are 1. Filter, 2. Switch and 3.
External filter”.
o Difference between if and switch:
o Here filter is like an IF, switch as switch.
o Differences between three filter stages.
Navs notes Page 105
IF SWITCH
Poor performance.
IF can write ‘n’ number of column in condition.
It have ‘n’ number of cases.
Better performance than IF.
SWITCH can only one condition can perform.
It can only have 128 cases.
FILTER SWITCH EXTERNAL FILTER Condition on multiple
columns.
It have,
o 1 – inputn – outputs1 – reject
Condition on single column.
It have,
o 1 – input128 – outputs1 - default
It is using by the GREP commands.
It have,
o 1 – input 1 – output no rejects
2010DataStage
Filter stage: “it having one input, n outputs, and one reject link”.
The symbol of filter is
Filter
Q: How the filter stage to send the data from source to target?
Design:
DS
Filter
OE
DS
DS
Step1:
Connecting to the oracle for extracting the EMP table from it.
Step2:
Filter properties
Predicates
Navs notes Page 106
T1
T2
Reject link
2010DataStage
o Where clauses = DEPT NO =10
Output link =1
o Where clauses = SAL > 1000 and SAL < 3000
Output link = 2
o Output rejects = true // it is for output reject data.
Link ordering
o Order of the following output links
Output:
o Mapping should be done for links of the targets we have.
Here, Mapping for T1 and T2 should be done separately for both.
Step3:
“Assigning a target files names in the target”.
It have no reject link, we must convert a link as reject link. Because it has ‘n’ number of
outputs.
DAY 34
Jobs on Filter and properties of Switch stage
Assignment Job 1:
a. Only DEPTNO 10 to target1?
b. Condition SAL>1000 and SAL<3000 satisfied records to target2?
Navs notes Page 107
2010DataStage
c. Only DEPTNO 20 where clause = SAL<1000 and SAL>3000 to target3?
d. Reject data to target4?
Design to the JOB1:
Filter
EMP_TBL
Filter
Step1: “For target1: In filter where clause for target1 is DEPTNO=10 and link order=0”.
Step2: “For target2: where clause = SAL>1000 and SAL<3000 and link order=1”.
Step3: “For target3: where clause= DEPTNO=20 and link order=0”.
Step4: “For target4: convert link into reject link and output reject link=true”.
Job 2:
a. All records from source to target1?
b. Only DEPTNO=30 to target2?
c. Where clause = SAL<1000 and SAL>3000 to target3?
d. Reject data to target4?
Navs notes Page 108
T1
T2
T3
T4
2010DataStage
Design to the JOB2:
Copy
EMP_TBL
Filter
Step1: “For target1 mapping should be done output links for this”.
Step2: “For target2 where clause = DEPTNO=30 and link order =0”.
Step3: “For target3 where clause = SAL<1000 and SAL>3000 and link order=1”.
Step4: “For target4 convert link into reject link and output reject link=true”.
Job 3:
a. All unique records of DEPTNO to target1?
b. All duplicates records of DEPTNO to target2?
c. All records to target3?
d. Only DEPTNO 10 records to target4?
e. Condition SAL>1000 & SAL<3000, but no DEPTNO=10 to target5?
Navs notes Page 109
T1
T2
T3
T4
2010DataStage
Design to the JOB3:
Filter
EMP_TBL
Filter
Step1: “For target1: where clause = keychange=1 and link order=0”.
Step2: “For target2: where clause = keychange=0 and link order=1”.
Step3: “For target3: mapping should be done output links for this”.
Step4: “For target4: where clause= DEPTNO=10”.
Step5: “For target5: in filter properties put output rows only once= true for where clause
SAL>1000 & SAL<3000”.
SWITCH Stage:
Navs notes Page 110
T1
T2
TT3
T4
K=1
K=0
T5
2010DataStage
“Condition on single column and it has only 1 – input, 128 – outputs and 1- default”.
Picture of switch stage:
Properties of Switch stage:
Input
o Selector column = DEPTNO
Cases values
o Case = 10 = 0 link order
o Case = 20 = 1
Options
o If no found = options (Drop/ fail/ output)
Drop= drops the data and continue the process.
Fail= if any records drops job aborts.
Output= to view reject data through the link.
DAY 35
External Filter and Combining
External Filter: “It is processes stage, which can perform filter by UNIX commands”.
It having 1-input, 1-output, and 1-reject link.
To perform a text file, first it must read in single record in the input.
Navs notes Page 111
2010DataStage
Example filter command: grep “newyork”.
Sequential File External Filter Data Set
External Filter properties:
o Filter command = grep “newyork”
o Grep –v “newyork” \\ other than new it filters.
Combining: “in DataStage combining can done in three types”.
They are
o Horizontal combining
o Vertical combining
o Funneling combining
Horizontal combining: combining primary rows with secondary rows based on primary key.
o This stage that perform by JOIN, LOOKUP, and MERGE.
These three stages differs with each other with respect to,
o Inputs requirements,
o Treatment of unmatched records, and
o Memory usage.
DAY 36
Horizontal Combining (HC) and Description of HC stages
Horizontal Combining (HC): “combining the primary rows with secondary rows based on
primary key”.
Navs notes Page 112
2010DataStage
Selection of primary table is situation based.
Here we can combine
Inner join,
Left outer join,
Right outer join, and
full outer join
If T1= {10, 20, 30} and T2= {10, 20, 40}
Inner Join: “Matched primary and secondary records”.
T1 T2
Left Outer Join: “Matched primary & secondary and unmatched primary records”.
T1 (T1 T2)
Right Outer Join: “Matched primary & secondary and unmatched secondary records”.
T2 (T1 T2)
Full Outer Join: “Matched primary & secondary and unmatched primary & unmatched
secondary records”.
T1 T2
Navs notes Page 113
ENO EName DNo
111 naveen 10 222 munna 20333 kumar 30
DNo DName LOC
10 IT HYD20 SE SEC40 SA WRL
HC
DNO DNAME LOC ENO ENAME
2010DataStage
Description of HC stages: “The description of horizontal combining is divided into nine
parts”. They are,
o Input names,
o Input output rejects,
o Join types,
o Input requirements with respect to sorting,
o De – duplication (removing duplicates),
o Treatment of unmatched records,
o Memory usage,
o Key column names, and
o Types of inner join.
The differences between join, lookup, and merge with respect to above nine points are
shown below.
Navs notes Page 114
JOIN LOOKUP MERGE
When we work on HC with JOIN the first SRC is left table, and last SRC is right table. And all middle SRC’s are intermediate tables.
The first link from source is primary/ input and remaining links are lookup/ references links.
The first table is master table and remaining tables are updates tables.
Input names:
Inner join,left outer join,right outer join, andfull outer join.
Inner Join
Left outer join
Inner join
Left outer join
Join Types:
N – inputs (inner, LOJ, ROJ)2 – inputs (FOJ)1 – output, and 1 – reject.
N – Inputs (normal)2 – inputs (sparse)1 – output, and 1 – reject
N – inputs1 – output(n – 1) rejects.
Input output rejects:
2010DataStage
DAY 37
LOOKUP stage (Processer Stage)
Lookup stage:
Navs notes Page 115
Primary: mandatory
Secondary: mandatory
Optional
Optional
Mandatory
Mandatory
:: Input Requirements with respect to sorting::
Primary: OK (nothing happens)
Secondary: OK
OK
Warnings
Warnings
OK
::De – Duplication (removing the duplicates)::
Primary: Drop (inner)Target (Left)
Secondary: Drop (inner)Target (right)
Drop, Target (continue), reject (unmatched primary records)
Drop
Drop, target (keep)
DropReject (unmatchedsecondary records)
:: Treatment of Unmatched Records::
Light memory Heavy memory Light memory
:: MEMORY USAGE::
Must be SAMEOptionalSame in case of lookup file set
Must be SAME
:: Key Column Names::
ALL ALL ANY
:: Type of Inner Join ::
2010DataStage
In real time projects, 95% of horizontal combining is used by this stage.
“Look up stage is for cross verification of primary records with secondary records”.
DataStage version8 supports four types of LOOKUP, they are
o Normal LOOKUP
o Sparse LOOKUP
o Range LOOKUP
o Case less LOOKUP
For example in simple job with EMP and DEPT tables:
Primary table as EMP with column consisting of EID, ENAME, DNO
Reference table as DEPT with column consisting of DNO, DNAME, LOC
DEPT table (reference/ lookup)
EMP table (Primary/ input) LOOKUP Data Set (target)
LOOKUP properties for two tables:
Navs notes Page 116
ENOENAME DNO
DNODNAMELOC
ENOENAME
DNAME
Primary Table
Target
2010DataStage
Key column for both tables
It can set by just drag from primary table to reference table to DNO column.
In tool bar of LOOKUP stage consists of constraints button, in that we have to select
Continue : this option for Left Outer Join.
Drop : it is to Inner Join.
Fail : its aborts job, if a primary unmatched records are their.
Reject : it’s captured the primary unmatched records.
Case less LOOKUP: In execution by default it acts as a case sensitive.
But we have a option to remove the case sensitive i.e.,
o Key type = case less.
DAY 38
Sparse and Range LOOKUP
Sparse LOOKUP:
Navs notes Page 117
Reference Table
2010DataStage
If the source is database, its supports only two inputs.
Normal lookup: “is cross verification of primary records with secondary at memory”.
Sparse lookup: “is cross verification of primary records with secondary at source level
itself”.
To set sparse lookup we must adjust key type as sparse in reference table only.
By default Normal LOOKUP is done in lookup stage.
Note: sparse lookup not support another reference when it is database.
But in ONE Case sparse LOOKUP stage can supports ‘n’ references. By taking lookup file set
Job1: a sequential file extracting a text file to load into lookup file set (lfs).
Sequential file Lookup file set
Here in lookup file set properties:
o Column names should same as in sequential file.
o Target file stored in .lfs extension.
o Address of the target must save to use in another job.
Job2: in this job we are using lookup file set as sparse lookup.
LFS LFS
……………………
Navs notes Page 118
2010DataStage
SF LOOKUP DS
In lookup file set, we must paste the address of the above lfs.
Lookup file supports ‘n’ references means indirectly sparse supports ‘n’ references.
Range LOOKUP:
“Range lookup is keeping condition in between the tables”.
How to set the range lookup:
In LOOKUP properties:
Select the check box for column you need to condition.
Condition for LOOKUP stage:
How to write a condition in the lookup stage?
o Go to tool bar constraint, there we will see condition box.
o In condition, for example: in.primary= “AP”
o For multiple links we can write multiple conditions for ‘n’ references.
DAY 39
Funnel, Copy and Modify stages
Funnel Stage:
Navs notes Page 119
2010DataStage
“It is a processing stage which performs combining of multiple sources to a target”.
To perform the funnel stage some conditions must to follow:
1. Columns should be same
2. Columns names also should be same
3. Columns names should be case sensitive
4. Data type should be same
Funnel stage it is process to append the records one table after the one, but above four
conditions has to be meet.
Simple example for funnel stage:
Funnel operation three modes:
Continues funnel : it’s random.
Sequence : collection of records is based on link order.
Sort funnel : it’s based on key column values.
Navs notes Page 120
ENO EN Loc GEN
111 naveen HYD M222 munna SEC F333 kumar BAN M
EMPID EName Loc Country Company GEN
444 IT DEL INDIA IBM 1555 SA NY USA IBM 0
TX
Copy/Modify
ENO EN ADD GEN
In this stage the column GEN M has to exchange into 1 and F=0;
In this column names has change as primary table.
2010DataStage
Copy Stage:
“It is processing stage which can be used from”.
1. Copying source data to multiple targets.
2. Charge the column names.
3. Drop the columns.
4. Stub stage.
NOTE: best for change column names and drop columns.
Modify Stage:
“It is processing stage which can perform”.
1. Drop the columns.
2. Keep the columns.
3. Change the column names.
4. Modify the data types.
5. Alter the data.
Oracle Enterprise Modify Data Set
From OE using modify stage send data into data set with respect to above five points.
In modify properties:
Specification: drop SAL, MGR, DEPTNO
o Here drops the above columns.
Navs notes Page 121
2010DataStage
Specification: keep SAL, MGR, DEPTNO
o Here accept the columns, remaining columns were drops.
At runtime: Data Set Management (view the operation process)
Specification: <new column name> DOJ=HIREDATE<old column>
o Here to change column name.
Specification: <new column name>DOJ=DATE_FROM_TIMESTAMP(HIREDATE)
<old column>
o Here changing the column name with data type.
DAY 40
JOIN Stage (processing stage)
Navs notes Page 122
2010DataStage
Join stage it used in horizontal combining with respect to input requirements, treatment of
unmatched records, and memory usage.
Join stage input names are left table, right table, and intermediate tables.
Join stage having n – inputs (inner, LOJ, ROJ), 2 – inputs (FOJ), 1- output, no
reject.
Types of Join stage are inner, left outer join, right outer join, and full outer join.
Input requirements with respect to sorting: it is mandatory in primary and secondary
tables.
Input requirements with respect to de – duplication: nothing happens means it’s OK
when de – duplication.
Treatment of unmatched records: in primary table when the option Inner its simple
drops and when it is LOJ will keep all records in target. And in secondary table in
Inner option it’s drops and it ROJ will keep all records in target.
Memory usage: light memory in join stage.
Key column names should be SAME in this stage.
All types of inner join will supports.
A simple job for JOIN Stage:
JOIN properties:
Need a key column
Navs notes Page 123
2010DataStage
o Inner JOIN, Left Outer JOIN comes in left table.
o Right Outer JOIN comes in right table.
o Full Outer JOIN comes both tables, in this no scope from third table that’s why
FOJ have two inputs.
In join stage when we sort with different key column names, that job can executes but
its effect on the performance (simply say WARNINGS will occurs)
We can change the column name by two types
Copy stage and with query statement.
Example of SQL query: select DEPTNO1 as DEPTNO, DN, and Loc from DEPT;
DAY 41
MERGE Stage (processing stage)
Navs notes Page 124
2010DataStage
Merge stage is a processing stage it perform horizontal combining with respect to input
requirements, treatment of unmatched records, and memory usage.
Merge stage input names are master and updates.
N – inputs, 1 – output, and (n – 1) rejects for merge stage.
Join types of this stage are inner join, and left outer join.
Input requirements with respect to sorting is mandatory to sort before perform merge
stage.
Input requirements with respect to de – duplication in the primary table it will get
warnings when we don’t remove the duplicates in primary table. And in secondary
table nothing will happens its OK when we don’t remove the duplicates.
Treatment of unmatched records in primary table Drop (drops), Target (keep) the
unmatched records of the unmatched primary table records. And in secondary table
drops and reject it captures the unmatched secondary table records.
In the merge stage the memory usage is LIGHT memory.
The key column names must be the SAME.
In type of inner join it compares in ANY update tables.
NOTE:
Static information stores in the master table.
All changes information stores in the update tables.
Merge operates with only two options
o Keep (left outer join)
o Drop (inner Join)
Simple job for MERGE stage:
Navs notes Page 125
PID PRD_DESC PRD_MANF11 indica tata22 swift maruthi 33 civic hundai 44 logon
PID PRD_SUPP PRD_CAT11 abc XXX33 xyz XXX55 pqr XXX77 mno XXX
PID PRD_AGE PRD_PRICE11 4 100022 9 120066 3 150088 9 1020
2010DataStage
Master Table Update (U1) Update (U2)
Master table
TRG
U1
U2
or
Reject (U1) Reject (U2)
In MERGE properties:
Merge have inbuilt sort = (Ascending Order/Descending Order)
Must to follow link order.
Merge supports (n-1) reject links.
NOTE: there has to be same number of reject links as update links or zero reject links.
Here COPY stage is acting as STUB Stage means holding the data with out sending
the data into the target.
DAY 42
Remove Duplicates & Aggregator Stages
Navs notes Page 126
2010DataStage
Remove Duplicates:
“It is a processing stage which removes the duplicates from a column and retains the
first or last duplicate rows”.
Sequential File Remove Duplicates Data Set
Properties of Remove Duplicates:
Two options in this stage.
o Key column= <column name>
o Dup to retain=(first/last)
Remove Duplicates stage supports 1 – input and 1 – output.
NOTE: for every n – input and n – output stages should must done mapping.
Aggregator:
“It is a processing stage that performs count of rows and different calculation between
columns i.e. group by same operation in oracle”.
SF Aggregator DS
Properties of Aggregator:
Grouping keys:
o Group= Deptno
Aggregator
o Aggregator type = count rows (count rows/ calculation/ re – calculation)
o Count output column= count <column name>
Navs notes Page 127
2010DataStage
1Q: Count the number of all records and deptno wise in a EMP table?
1 Design:
OE_EMP Copy of EMP Counting rows of deptno TRG1
Generating a column counting rows of created column TRG2
For doing some group calculation between columns:
Example:
Select group key
Group= DEPTNO
- Aggregation type = calculation
- Column for calculation = SAL <column name>
Operations are
Maximum value output column = max <new column name>
Minimum value output column = min <new column name>
Sum of column = sum <new column name> and so on.
Here, doing calculation on SAL based on DEPTNO;
2Q In Target one dept no wise to find maximum, minimum, and sum of rows, and in
target two company wise maximum?
2 Design:
Navs notes Page 128
2010DataStage
OE_emp copy of emp max, min, sum of deptno trg1
Company: IBM max of IBM trg2
3Q: To find max salary from emp table of a company and find all the details of that?
&
4Q: To find max, min, sum of salary of a deptno wise in a emp table?
3 & 4 Design: dummy dno=10
compare
emp
max(deptno) dno=20
UNION ALL diving
compare dummy dno=30
copy
min(deptno)
company: IBM compare
maximum SAL with his details
max (IBM)
DAY 43
Slowly Changing Dimensions (SCD) Stage
Before SCD we must understand: types of loading
Navs notes Page 129
2010DataStage
1. Initial load
2. Incremental load
Initial load: complete dump in dimensions or data warehouse i.e., target also before
data is called Initial load.
The subsequent is alter is called incremental load i.e., coming from OLTP also source
is after data.
Example: #1
Before data (already data in a table)
CID CNAME ADD GEN BALANCE Phone No AGE
11 A HYD M 30000 9885310688 24
After data (update n insert at source level data)
CID CNAME ADD GEN BALANCE Phone No AGE
11 A SEC M 60000 9885865422 25
Column fields that have changes types:
Address – slowly change
Balance – rapid change
Phone No – often change
Age – frequently
Example: #2
Before Data:
CID CNAME ADD
Navs notes Page 130
2010DataStage
11 A HYD
22 B SEC
33 C DEL
After Data: (update ‘n’ insert option loading a table)
CID CNAME ADD
11 A HYD
22 B CUL
33 D PUN
Extracting after and before data from DW (or) database to compare and upsert.
We have SIX Types of SCD’s are there, they are
SCD – I
SCD – II
SCD – III
SCD – IV or V
SCD – VI
Explanation:
SCD – I: “it only maintains current update, and no historical data were organized”.
As per SCD – I, it updates the before data with after data and no history present after the
execution.
SCD – II: “it maintains both current update data and historical data”. With some special
operation columns they are, surrogate key, active flag, effect start date, and effect end date;
In SCD – II, not having primary key that need system generated primary key, i.e.,
surrogate key. Here surrogate key acting as a primary key.
Navs notes Page 131
2010DataStage
And when SCD – II performs we get a practical problem is to identify old and current
record. That we can solve by active flag: “Y” or “N”.
In SCD – II, new concepts are introduced here i.e., effect start date (ESDATE) and
effect end date (EEDATE).
Record version : it is concept that when the ESDATE and EEDATE where not able to
use is some conditions.
Unique key : the unique key is done by comparing.
SCD – III: SCD – I (+) SCD – II “maintain the history but no duplicates”.
SCD – IV or V: SCD – II + record version
“When we not maintain date version then the record version useful”.
SCD – VI: SCD – I + unique identification.
Example table of SCD data:
SID CID CNAME ADD AF ESDATE EEDATE RV UID
1 11 A HYD N 03-06-06 29-11-10 1 1
2 22 B SEC N 03-06-06 07-09-07 1 2
3 33 C DEL Y 03-06-06 9999-12-31 1 3
4 22 B DEL N 08-09-07 29-11-10 2 2
5 44 D MCI Y 08-09-07 9999-12-31 1 5
6 11 A GDK Y 30-11-10 9999-12-31 2 1
7 22 B RAJ Y 30-11-10 9999-12-31 3 2
8 55 E CUL Y 30-11-10 9999-12-31 1 8
Table: this table is describing the SCD six types and the description is shown above.
DAY 44
SCD I & SCD II (Design and Properties)
Navs notes Page 132
2010DataStage
SCD – I: Type1 (Design and Properties):
Transfer job Load job
10,20,30
OE_DIM before fact DS_FACT 10, 20, 40 10, 20, 40
DS_TRG_DIM OE_UPSERT
10, 20, 40 After dim 10,20, 40 -update and insert
OE_SRC DS_TRG_DIM
In oracle we have to create table1 and table2,
Table1:
Create table SRC(SNO number, SNAME varchar2(25));
o Insert into src values(111, ‘naveen’);
o Insert into src values(222, ‘munna’);
o Insert into src values(333, ‘kumar’);
Table2:
Create table DIM(SKID number, SNO number, SNAME varchar2(25));
o No records to display;
Processes of transform job SCD1:
Step 1: Load plug-in Meta data from oracle of before and after data as shown in the above
links that coming from different sources.
Step 2: “SCD1 properties”
Navs notes Page 133
2010DataStage
Fast path 1 of 5: select output link as:
Fast path 2 of 5: navigating the key column value between before and after tables
Fast path 3 of 5: selecting source type and source name.
Source type: source name:
NOTE: for every time of running the program we should empty the source name i.e.,
empty.txt, else surrogate key will continue with last stored value.
Fast path 4 of 5: select output in DIM.
For path 5 of 5: setting the output paths to FACT data set.
Navs notes Page 134
SNOSNAME
AFTER
KEY EXPR COLUMN N PURPOSE SKID surrogate keyAFTER.SNO SNO business key
SNAME Type1
BEFORE
Flat file D:\study\navs\empty.txt
fact
SNOSNAME
AFTER
Derivation COLUMN N PURPOSE next sk() SKID surrogate keyAFTER.SNO SNO business keyAFTER.SNAME SNAME Type1
DIM
SNOSNAME
AFTER
Derivation COLUMN N BEFORE.SKID SKID
AFTER.SNO SNO
FACT
SKIDSNOSNAMEBEFORE
2010DataStage
Step 3: In the Next job, i.e. in load job if we change or edit in the source table and when you
are loading into oracle we must change the write method = upsert in that we have two options
they are, -update n insert \\ if key column value is already.
-insert n update \\ if key column value is new.
Here SCD I result is for the below input
Before table
Target Dimensional table of SCD I
After table
SCD – II: (Design and Properties):
Navs notes Page 135
CID CNAME SKID10 abc 120 xyz 230 pqr 3
CID CNAME10 abc20 nav40 pqr
CID CNAME SKID10 abc 120 nav 240 pqr 3
2010DataStage
Transfer job Load job10,20,30
before
OE_DIM fact DS_FACT 10, 20, 20, 30, 40 10, 20, 20, 30, 40
DS_TRG_DIM OE_UPSERT
10, 20, 40 After dim 10, 20, 20, 30, 40 -update and insert
OE_SRC DS_TRG_DIM
Step 1: in transformer stage:
Adding some columns to the to before table – to covert EEDATE and ESDATE columns into
time stamp transformer stage to perform SCD II
In TX properties:
In SCD II properties:
Navs notes Page 136
SKIDSNOSNAMEESDATEEEDATEACF
BEFORE
Derivation COLUMN NAMBEFORE.SKID SKID BEFORE.SNO SNO BEFORE.SNAME SNAMETS_to_D(BEFORE.ESDATE) ESDATETS_to_D(BEFORE.EEDATE) EEDATEBEFORE.ACF ACF
BEFORE_TX
2010DataStage
Fast path 1 of 5: select output link as:
Fast path 2 of 5: navigating the key column value between before and after tables
Fast path 3 of 5: selecting source type and source name.
Source type: source name:
NOTE: for every time of running the program we should empty the source name i.e.,
empty.txt, else surrogate key will continue with last stored value.
Fast path 4 of 5: select output in DIM.
Date from Julian (Julian day from day (current date ()) – 1)
For path 5 of 5: setting the output paths to FACT data set.
Navs notes Page 137
fact
SNOSNAME
AFTER KEY EXPR COLUMN N PURPOSE SKID surrogate keyAFTER.SNO SNO business key
SNAME Type2 ESDATE experi date ESDATE expire date ACF current indicator
BEFORE
Flat file D:\study\navs\empty.txt
SNOSNAME
AFTER
SNOSNAME
AFTER
BEFORE
Derivation COLUMN N PURPOSE Expires next sk() SKID surrogate key -AFTER.SNO SNO business key -AFTER.SNAME SNAME Type2 -curr date() ESDATE experi date -“9999-12-31” ESDATE expire date (…..)“Y” ACF CI “N”
DIM
2010DataStage
Step 3: In the Next job, i.e. in load job if we change or edit in the source table and when you
are loading into oracle we must change the write method = upsert in that we have two options
they are, -update n insert \\ if key column value is already.
-insert n update \\ if key column value is new.
Here SCD II result is for the below input
Before table
Target Dimensional table of SCD II
After table
DAY 45
Navs notes Page 138
SKIDSNOSNAMEESDATEEEDATEACF
CID CNAME SKID ESDATE EEDATE ACF10 abc 1 01-10-08 99-12-31 Y20 xyz 20 01-10-08 99-12-31 Y30 pqr 30 01-10-08 99-12-31 Y
CID CNAME10 abc20 nav40 mun
CID CNAME SKID ESDATE EEDATE ACF10 abc 1 01-10-08 99-12-31 Y20 xyz 2 01-10-08 09-12-10 N20 xyz 4 10-12-10 99-12-31 Y30 pqr 3 01-10-08 99-12-31 Y40 mun 5 01-10-08 99-12-31 Y
Derivation COLUMN NAME BEFORE.SKID SKID AFTER.SNO SNOAFTER.SNAME SNAME BEFORE.ESD ESDATE BEFORE.EED ESDATE BEFORE.ACF ACF
FACT
2010DataStage
Change Capture, Change Apply & Surrogate Key stages
Change Capture Stage:
“It is processing stage, that it capture whether a record from table is copy or edited or
insert or to delete by keeping the code column name”.
Simple example of change capture:
Change_capture
Properties of Change Capture:
Change keys
o Key = EID (key column name)
Sort order = ascending order
Change valves
o Values =? \\ ENAME
o Values =? \\ ADD
Options
o Change mode = (explicit keys & values / explicit keys, values)
o Drop output for copy = (false/ true) “false – default ”
o Drop output for delete = (false/ true) “false – default”
o Drop output for edit = (false/ true) “false – default”
o Drop output for insert = (false/ true) “false – default”
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2010DataStage
Copy code = 0
Delete code = 2
Edit code = 3
Insert code = 1
Code column name = <column name>
o Log statistics = (false/ true) “false – default”
Change Apply Stage:
“It is processing stage, that it applies the changes of records of a table”.
Change Apply
Properties of Change Apply:
Change keys
o Key = EID
Sort order = ascending order
Options
o Change mode = explicit key & values
o Check value columns on delete = (false/ true) “true - default”
o Log statistics = false
o Code column name = <column name> \\ change capture and this has to be
SAME for apply operations
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2010DataStage
SCD II in version 7.5.x2
Design of thatESDATE=current date ()
EEDATE= “9999-12-31”
Key=EID ACF= “Y”
-option: e k & v
Before.txt c=3
c=all
after.txt
key= EID
-option: e k & v
before.txt
ESDATE- current date ()
EEDATE- if c=3 then DFJD(JDFD(CD())-1)
else EEDATE = “9999-12-31”
ACF- if(c=3) then “N” else “Y”
SURROGATE KEY Stage:
In version 7.5.x2: “identifying last value which generated for the first time compiling and
running the job in surrogate key stage, for that reason in version 7 we have to do a another job
to store a last generated value”.
And that job in version 7.5.x2: design
SF Sk copy ds
Tail peek
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2010DataStage
In this job, a surrogate key stage used for generates the system key column values that
are like primary key values. But it generate at first compile only.
But by taking tail stage with that we tracing the last value and storing into the peek
stage that is in buffer.
With that buffer value we can generate the sequence values that are surrogate key in
version 7.5.x2.
In version 8.0:
“The above problem with version7 is over comes by version 8.0 surrogate key by
taking an empty text(empty.txt) file and storing last value information in that file, and by using
that it generates the sequence values”
Before.txt SK Data Set
Properties of SK version8:
Option 1: generated output column name = skid
Source name = g:\data\empty.txt
Source type = flat file
Option 2: database type= oracle (DB2/ oracle)
Source name = sq9 (in oracle – create sequence sq9)\\ it is like empty.txt
Password= tiger
User id= scott
Server name= oracle
Source type = database sequence
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2010DataStage
DAY 46
DataStage Manager
Export:
“Export is used to save the group of jobs for the export purpose that where we want”.
Navigation - “how to export”?
DataStage toolbar
Change selection: or or
o Job components to export
Here there are three options are
- Export job designs with executables(where applicable)
- Export job designs without executables
- Export job executables without designs
o Export to file
Where we want locate the export file.
o Type of export
By two options we can export file
- dsx 7 – bit encoded
- xml
Import:
“It is used to import the .dsx or .xml extensions to a particular project and also to
import some definitions as shown below”.
Options of import are
o DataStage components…
Navs notes Page 143
ADD REMOVE SELECT ALL
Source name\.....
dsx
2010DataStage
o DataStage components (xml)…
o External function definitions
o Web services function definitions
o Table definitions
o IMS definitions
In IMS two options are,
Database description (DBD)
Program Specification Block (PSB / PCB)
In DataStage components..
o Import from file
Import all overwrite without query
Import selected perform impact analysis
Generate Report:
“It is for to generate report to a job or a specific, that it generates a report to a job
instantly”.
For that, go to
File
o Generate report
Report name
Options
Use default style sheet
Use custom style sheet
After finishing the settings:
It’s generates in default position
“/reportingsendfile/ send file/ tempDir.tmp”
Navs notes Page 144
Give the source name to import ….
2010DataStage
Node Configuration:
Q: To see nodes in a project:
o Go to run director
Check in logs
Double click on main program: APT config file
Q: What are Node Components?
1. Node name – logical CPU name.
2. Fast name – server name or system name.
3. Pools – logical area where stages are executed.
4. Resource – memory associated with node.
o Node components stores in the disc’s permanent in the below address.
“c:\ibm\information server\server\parasets”
o Node components stores temporary is the below address.
“c:\ibm\information server\scratch”
Q: What node that handles to run each and every job and name of the configuration file?
o Every job runs on APT node as on below name that is default for every job.
o Name of configuration file is C:\ibm\.........\default.apt
Q: How to run a job on specific configuration file?
o Job properties
Parameters
Add environment variables
o Parallel
Compiler
Config file (Add $APT_CONFIG_FILE)
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2010DataStage
Q: How to create a new Node configuration File?
o Tools
Configurations
There we see
o
o Default.apt will have the single node information.
o We can create new node by option NEW
o
Save the things after creating new nodes
By, save configuration As
o NOTE: Best 8 or 16 nodes is to create in a project, and
2^0,2^1(say) CPU’s have & so on.
Q: If uni processing system with 1 CPU needs minimum 1 node to run a job then for SMP
with 4 CPU needs how many minimum nodes?
o Only 1 node.
Advanced Find:
“It is the new feature to version8”
It consists of to find objects of a job like list shown below
1. Where used,
2. Dependency,
3. Compared report.
Q: How to run a job in a job?
Navigation for how to run a job in a job
Job properties
o Job control
Select a job
Navs notes Page 146
Default.apt
NEW
2010DataStage
-------------
------------- here, Job Control Language (JCL) script presents.
-------------
o Dependencies
Select job (first compile this job before the main
job)
Q: Repository of Advance Find (means palate of advance find)?
o Name to find:
o Folder to search:
o Type
o Creation
o Last modification
o Where used
Find objects that use any of the following objects.
Options: Add, remove, remove all
o Dependencies of job
Q: Advance Find of repository through tool bar?
o Cross project compare….
o Compare against
o Export
o Multiple job compile
o Add to palate
o Create copy
o Locate in tree
o Find dependencies
Q: How to find dependency in a job?
o Go to tool bar
Navs notes Page 147
Nav*
D:\datastage\
2010DataStage
Repository
Find dependency: all types of a job
DAY 47
DataStage Director
DS Director maintains:
Schedule
Monitor
Views
o Job view
o Status view
o Log view
Message Handling
Batch jobs
Unlocking
Schedule:
“Schedule means a job can run in specific timings”
To set timings for that,
o Right click on job in the DS Director
Click on “add to schedule…”
And set the timings.
In real time, specific the job sequence by some tools shown below
o Tools to schedule jobs (its happen the production only)
Control M
Cron tab
Autosys
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2010DataStage
Purge:
“It means cleaning or wash out or deleting the already created logs”
- In job can we clear
- Job logs having a option is FILTER. By right clicking we can
filter.
Navigation for set the purge.
o Tool bar
Job
- Clear log (choose the option)
o Immediate purge
o Auto purge
Monitor:
“It shows the Status of job, numbers of row where executed, started at (time), elapsed
time (i.e. rows/sec), percentage used by CPU)”
Navigation for job that how to monitor.
o Right click on job
Click monitor
“it shows performance of a job”
Like below figure for a simple job.
Navs notes Page 149
Finished 6 sys time 00:00:03 2 =9Finished 6 sys time 00:00:03 2 =7Finished 6 sys time 00:00:03 2 =0
Status No. rows started at elaspsed time rows/sec %CPU
2010DataStage
NOTE: Based on this we can check the performance tuning of a stage in a particular
job.
Reasons for warnings:
Default warnings in sequential file are
1. Field “<column name>” has import error and no default value; data : { e i d },
at offset: 0
2. Import warnings at record 0.
3. Import unsuccessful at record 0.
o These three warnings can solve by a simple option in sequential file, i.e.,
First line is column names= set as true.(here default option is false)
Missing record delimiter “\r\n”, saw EOF instead (format mismatch)
When we working on look-up, in the secondary stage have duplicates we with get
warning.
Where these is length miss match, like in source length (10) and target (20).
When sorting for different key column in join.
When second stage in merge.
Abort a job:
Q: How can we abort a job conditionally?
Conditionally
o When we Run a job
Their we can keep a constraint
Like warnings
o No limit
o Abort job after:
In transformer stage
o Constraint
Otherwise/log
Navs notes Page 150
5
2010DataStage
Abort after rows: 5 (if 5 records not meet the constraint it’s
simple aborts the job)
We can keep constraint same like this only in Range Lookup.
Message Handling:
“If the warnings are failed to handle then we come across the message handling”
Navigation for how to add rule set a message handle the warnings.
Jog logs
o Right click on a warning
Add rule to message handler
Two options
Suppress from log
Demote to information
Choose any one of above option and add rule.
Batch jobs:
“Executing set of jobs in a order”
Q: How to create a Batch?
Navigation for creating a batch
DS Director
o Tools
Batch
New (give the name of batch)
Add jobs in created job batch
o Just compile after adding in new batch.
Allow multiple instances:
“Same job can open by multiple clients and run the job”
If we not enable the option it will open in a read only that you can’t edit.
But a job can execute by multiple users at the same time in director.
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2010DataStage
Navigation for enable the allow multiple instance
Go to tool bar in DS Designer
o Job properties
Check the box on “allow multiple instances”
Unlock the jobs:
“We can unlock the jobs for multiple instances by release all the permissions”
Navigation for unlock the job
DS Director
Tool bar
o Job
Cleanup resources
Processes
Show by job
Show all
o Release all
For global to see PIDs for jobs, for that
DS Administrator
o General
Environment variables
Parallel
o Reporting
Add (APT_PM_SHOW_ PIDS)
Set as (true/false)
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2010DataStage
DAY 48
Web Console Administrator
Components of administrator:
Administration:
o User & group
Users
User name & password is created here.
And assigning permissions
Session managements:
o Active sessions
For admin
Reports:
o DS
INDIA (server/system name)
View report.
We can create the reports.
Domain Management:
o License
Update the license here
Upload to review
Scheduling management:
“It is know what user is doing from part”
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2010DataStage
o Scheduling views
New
schedule | Run
creation task run | last update
DAY 49
Job Sequencing
Stages of job sequencing:
“It is for executing jobs in sequence that we can schedule job sequencing”
Or
“Its control the order of execution jobs”
A simple job will process in below process.
o Extract
o Transform
o Load
o Master jobs: “its control the order of execution”.
Important stages in job sequencing are
1. Job activity
2. Sequencer
3. Terminator activity
4. Exception handler
5. Notification activity
6. Wait for file activity
Job Activity:
“It is job activity that holds the job and it have 1-input and n-outputs”
Job activity
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2010DataStage
How the Job Activity drag into design canvas?
- In two methods we can,
1. Go to tool bar – view – repository – jobs – just drag the job to the canvas.
2. Go to tool bar – view – palate – job activity – just drag the icon to the canvas.
Simple job:
Student Sequencer student rank
Terminator activity
Properties of Job Activity:
Load a job what you want in active
o Job name:
Execution action: Do not check point Run
options - (Run/Reset if required, than run/ Validate only/ Reset only)
Check Point:
“Job has re-started where it aborted it is called check point”
It is special option that we must enable manually
Go to
o Job properties of DS Designer
Enable check point
Navs notes Page 155
D:\DS\scd_job
RUN
OK
WAR
FAIL
2010DataStage
Parameter mapping:
“If job have already some parameters to that we can map to the another job if we need”
Triggers:
“It holds the link expression type that how to act”
And some more options in “Expression type”
- Unconditional “N/A (its default)”
- Otherwise “N/A”
- User Status = “<user define message>”
- Custom-(conditional) - “custom”
Terminator Activity:
“It is stage that handles the error if it fails”
Properties:
It consists of two options: for if any sub ordinate jobs are still running.
Its for job failure
o Send STOP requests to all Running Jobs
And wait for all jobs to finish
It’s for server downs in between the process running.
o Abort without sending STOP requests
Wait for all jobs to finish first.
Navs notes Page 156
Name of Expression type Expression output link
OK OK-(conditional) “executed OK”
WAR WAR-(conditional) “execution finished with warnings”
Fail Failed-(conditional) “execution failed”
2010DataStage
Sequencer: “it holds multiple inputs and multiple outputs”
It has two options or modes: ALL – it’s for OK & WAR links
ANY – it’s for FAIL (‘n’ number of links)
Exception handler:
“It handles the server interrupts”
we don’t connect any stage here it will separate in a job
A simple job for exception handler:
Exception handler Notification activity Terminator activity
Exception handler properties:
“Its have only general information”
Notification Activity:
“It is sending acknowledgement in between the process”
Option to fill in the properties:
SMTP Mail server Name:
Senders email address:
Recipients email address:
Email subject:
Attachments:
Email body:
Wait for file Activity: “To place the job in pause”
Navs notes Page 157
ALLANYALL
D:\DS\SCD_LOAD browse file
2010DataStage
File name:
Two options: wait for file to appear
Wait for file to disappear
Timeout length (hh:mm:ss) Do not timeout (no time length for the above options)
DAY 50
Performance tuning w.r.t partition techniques & Stages
Partition techniques: “are two categories”
Key based:
1. Hash
2. Modulus
3. DB2
4. Range
Key less:
1. Same
2. Round Robin
3. Entire
4. Random
In key based partition technique:
DB2 is used when the target is database.
DB2 and Range techniques are used rarely.
Hash partition technique:
o It is selected when number of key columns will be there. i.e., key columns (>1)
and hetro data types (means different different data types)
o Other than this situation we can select “modulus partition technique”.
Modulus partition technique:
o It distributes the data based on mod values.
o And mod formula is MOD(value/ Number of nodes)
Navs notes Page 158
2010DataStage
NOTE: Modulus is having high performance than Hash, because the way its groups the data
and based on the mod value.
NOTE: But modules can only be selected, if the only one key column and only one data type
that is only integer (data type).
In Key less partition technique:
Same: is never distributes the data, but is carry previous technique that continuous.
Entire: will distribute the same group of records to all nodes. That is the purpose of
avoiding the mismatch records in between the operation.
Round Robin: it is for generated stage like Column Generator and so on is associated
this partition technique.
o It is the best partition technique than comparing to random.
Random: all key less partition techniques stages are used this technique its default.
Performance tuning w.r.t Stages:
If when Sorting already perform then JOIN stage we can use.
Else LOOKUP stage is the best.
LOOKUP FILE SET: is options use to remove duplicates in lookup stage.
SORT stage: if complex sort : go to Stage sort
Else: go to link sort.
Remove Duplicates: the data already sort – Remove duplicates stage
Sorting and remove duplicates – go to link sort (unique)
Constraints: when operation and constraints needed – go to Transformer stage
Else only constraints – simply go to FILTER stage.
Navs notes Page 159
2010DataStage
Conversions: Modify stage and Transformer stage (it takes more compile time).
DAY 51
Compress, Expand, Generic, Pivot, xml input & output Stages
Compress Stage:
“It is a processing stage that compresses the records into single format means in single
file or it compresses the records into zip”.
It supports – “1 input and 1 output”.
Properties:
Stage
o Options
Command= (compress/gzip)
Input
o <do nothing>
Output
o Load the ‘Meta’ data of the source file.
Expand Stage:
“It is a processing stage the extract the compress data or its extract the zip data into
unzip data”.
It supports – “1 input and 1 output”.
Navs notes Page 160
2010DataStage
Properties:
Stage:
o Options : - command= (uncompress/gunzip)
Input:
o <do nothing>
Output:
o Load the Meta data of the source file for the further process.
Encode Stage:
“It is processing stage that encodes the records into single format with the support of
command line”.
It supports – “1-input and 1-output”.
Properties:
Stage
o Options: Command line = (compress/ gzip)
Input
o <do nothing>
Output
o Load the ‘Meta’ data of the source file.
Decode Stage:
“It is processing stage that decodes the encoded data”.
It supports – “1-intput and 1-output”.
Navs notes Page 161
2010DataStage
Properties:
Stage
o Options: command line = (uncompress/gunzip)
Output
o Load the ‘Meta’ data of the source file.
Generic Stage:
“It is processing stage that holds any operator can call here, but it must and should full
fill the properties”.
It supports – “n- inputs and n-outputs, but no rejects”
When compiling the job, the job related OSH code will generated.
Generic stage can call the operator on the datastage.
Its purpose is migration serve jobs to parallel jobs (IBM has x- migrator that converts
into 70%)
And it can call ANY operator here, but it must full fill the properties.
Properties:
Stage
o Options
Operator: copy (we can write any stage operator here)
Input
o <do nothing>
Output
o Load the Meta data of the source file.
Pivot Stage:
“It is processing stage that converts rows into columns in a table”.
Its supports – “1-input and 1-output”.
Navs notes Page 162
2010DataStage
Properties: Stage – <do nothing>
Input: <do nothing>
Output:
XML Stages:
“It is real time stage that the data stores in single records or in aggregator with in the
xml format”.
And XML Stage divided into two types, they are
1. XML Output
2. XML Input
XML Input:
“”.
Navs notes Page 163
REC <col_n with comma separated> varchar 25
Column name Derivation SQL Type Length
