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Data Warehousing

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Overview

• What is data warehouse?• Why data warehouse?• Data reconciliation – ETL process • Data warehouse architectures• Star schema – dimensional modeling• Data analysis

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What is Data Warehouse?

• Defined in many different ways, but not rigorously.

– A decision support database that is maintained separately from

the organization’s operational database

– Support information processing by providing a solid platform of

consolidated, historical data for analysis.

• “A data warehouse is a subject-oriented, integrated, time-

variant, and nonvolatile collection of data in support of

management’s decision-making process.”—W. H. Inmon

• Data warehousing:

– The process of constructing and using data warehouses

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Data Warehouse—Subject-Oriented

• Organized around major subjects, such as customer,

product, sales

• Focusing on the modeling and analysis of data for

decision makers, not on daily operations or

transaction processing

• Provide a simple and concise view around particular

subject issues by excluding data that are not useful in

the decision support process

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Data Warehouse—Integrated• Constructed by integrating multiple,

heterogeneous data sources– relational databases, flat files, on-line transaction

records

• Data cleaning and data integration techniques are applied.– Ensure consistency in naming conventions, encoding

structures, attribute measures, etc. among different data sources

• E.g., Hotel price: currency, tax, breakfast covered, etc.

– When data is moved to the warehouse, it is converted.

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Data Warehouse—Time Variant

• The time horizon for the data warehouse is

significantly longer than that of operational systems

– Operational database: current value data

– Data warehouse data: provide information from a

historical perspective (e.g., past 5-10 years)

• Every key structure in the data warehouse

– Contains an element of time, explicitly or implicitly

– But the key of operational data may or may not contain

“time element”

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Data Warehouse—Nonvolatile• A physically separate store of data transformed

from the operational environment

• Operational update of data does not occur in the

data warehouse environment

– Does not require transaction processing, recovery,

and concurrency control mechanisms

– Requires only two operations in data accessing:

• initial loading of data and access of data

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Trends in Organisations that encourage the need for data warehousing

• No single system of record• Multiple systems are not synchronized• Organisations want to analyse the activities in a balanced way• Customer relationship management• Supplier relationship management

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Need for Data Warehousing• Integrated, company-wide view of high-quality

information (from different databases)• Separation of operational and informational systems

and data (for improved performance)

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Operational & Informational System

The need to separate operational and informational

systems is based on three primary factors:

• A data warehouse centralizes data that are scattered throughout disparate operational systems and make them a available for decision support applications

• A properly designed data warehouse adds value to data by improving their quality

• A separate data warehouse eliminates much of contention for resources that result when informational application confounded with operational processing

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Data Reconciliation• Typical operational data is:

– Transient – not historical– Not normalised (perhaps due to denormalisation for performance)– Restricted in scope – not comprehensive– Sometimes poor quality – inconsistencies and errors

• After ETL (Extract, Transform, Load), data should be:– Detailed – not summarized yet– Historical – periodic– Normalised – 3rd normal form or higher– Comprehensive – enterprise-wide perspective– Timely – data should be current enough to assist decision-making– Quality controlled – accurate with full integrity

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The ETL Process/ Data Reconciliation Main Steps

• Capture/Extract

• Scrub or data cleansing

• Transform

• Load and Index

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Static extract = capturing a snapshot of the source data at a point in time

Incremental extract = capturing changes that have occurred since the last static extract

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Fixing errors:Fixing errors: misspellings, erroneous dates, incorrect field usage, mismatched addresses, missing data, duplicate data, inconsistencies

Also:Also: decoding, reformatting, time stamping, conversion, key generation, merging, error detection/logging, locating missing data

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Record-level:Record-level:Selection – data partitioningJoining – data combiningAggregation – data summarization

Field-level:Field-level: single-field – from one field to one fieldmulti-field – from many fields to one, or one field to many

17Refresh mode:Refresh mode: bulk rewriting of target data at periodic intervals

Update mode:Update mode: only changes in source data are written to data warehouse

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Data Warehouse Architectures

• Generic Two-Level Architecture

• Independent Data Mart

• Dependent Data Mart and Operational Data Store

• Logical Data Mart and @ctive Warehouse

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Generic two-level architecture

E

T

LOne company-wide warehouse

Periodic extraction data is not completely current in warehouse

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Independent data mart Data marts:Data marts:Mini-warehouses, limited in scope

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T

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Separate ETL for each independent data mart

Data access complexity due to multiple data marts

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Dependent data mart with operational data store

ET

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Single ETL for enterprise data warehouse (EDW)(EDW)

ODS ODS provides option for obtaining current data

Dependent data marts loaded from EDW

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ET

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Near real-time ETL for @active Data Warehouse

ODS ODS and data warehousedata warehouse are one and the same

Data marts are NOT separate databases, but logical views of the data warehouse Easier to create new data marts

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Data CharacteristicsStatus vs. Event Data

Status

Status

Event – a database action (create/update/delete) that results from a transaction

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Data CharacteristicsData CharacteristicsTransient vs. Transient vs. Periodic DataPeriodic Data

Changes to existing records are written over previous records, thus destroying the previous data content

Data are never physically altered or

deleted once they have been added to

the store

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star schemastar schemaFact tables contain factual or quantitative data

Dimension tables contain descriptions about the subjects of the business

1:N relationship between dimension tables and fact tables

Star Schema: Simple database design in which dimensional data are separated from fact data. Excellent for queries, but bad for online transaction processing

Dimension tables are denormalized to maximize performance

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Star schema example

Fact table provides statistics for sales broken down by product, period and store dimensions

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On-Line Analytical Processing (OLAP)• The use of a set of graphical tools that provides

users with multidimensional views of their data and allows them to analyze the data using simple windowing techniques

• Relational OLAP (ROLAP)– Traditional relational representation

• Multidimensional OLAP (MOLAP)– Cube structure

• OLAP Operations– Cube slicing – come up with 2-D view of data– Drill-down – going from summary to more detailed views

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Data Warehouse vs. Operational DBMS

• OLTP (on-line transaction processing)– Major task of traditional relational DBMS

– Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc.

• OLAP (on-line analytical processing)– Major task of data warehouse system

– Data analysis and decision making

• Distinct features (OLTP vs. OLAP):– User and system orientation: customer vs. market

– Data contents: current, detailed vs. historical, consolidated

– Database design: ER + application vs. star + subject

– View: current, local vs. evolutionary, integrated

– Access patterns: update vs. read-only but complex queries

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OLTP vs. OLAP OLTP OLAP

users clerk, IT professional knowledge worker

function day to day operations decision support

DB design application-oriented subject-oriented

data current, up-to-date detailed, flat relational isolated

historical, summarized, multidimensional integrated, consolidated

usage repetitive ad-hoc

access read/write index/hash on prim. key

lots of scans

unit of work short, simple transaction complex query

# records accessed tens millions

#users thousands hundreds

DB size 100MB-GB 100GB-TB

metric transaction throughput query throughput, response

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Slicing a data cube

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Example:Drill-down

Summary report

Drill-down with color added

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Data Warehouse Usage

• Three kinds of data warehouse applications– Information processing

• supports querying, basic statistical analysis, and reporting

using crosstabs, tables, charts and graphs

– Analytical processing

• multidimensional analysis of data warehouse data

• supports basic OLAP operations, slice-dice, drilling, pivoting

– Data mining

• knowledge discovery from hidden patterns

• supports associations, constructing analytical models,

performing classification and prediction, and presenting the

mining results using visualization tools