Architecture and Infrastructure

Module 2G.Anuradha

What is architecture?

• The structure that brings all the components of a data warehouse together is known as the architecture.

• Many factors affect the architecture of a DW– Integrated data– Data preparation and storing– Data delivery– Technology

• Comprehensive blueprint

Architecture in 3 major areas

• Data acquisition• Data storage• Information delivery

Distinguishing characteristics of architecture

• Different Objectives and Scope– For providing strategic information DW should have elaborate

architecture– Scope depends on the sources used in the acquisition region

• Data Content– Dealing with historical, read only data

• Complex Analysis and Quick Response– Drill down, roll up, slice, dice, what if scenarios

• Flexible and Dynamic– Design should be dynamic after designing as well

• Metadata-driven– Every movement is trapped in it.

Test your fundas

ACROSS1. Business dimension(5)6. Smaller than DW(8)7. Combining data from different operational systems(10)8. Initial loading(7)

DOWN2. Remove useful information from operational data(10)3. Monitoring the entire function (10)4. Historical(8)5. Data about entire warehouse(8)

Solution

Architecture supporting the flow of data

Data Source(internal & External)

Data StagingTransformation

CleansingIntegration of Data

Data StorageLoading of data from Staging

AreaStoring for Information

Delivery

MetadataStorage mechanism for data about data

Information DeliveryDependent data marts, MDDBs, Query and

reporting facilities

Management and control module

• Umbrella component having two important functions– Monitor all ongoing operations– Problem recovery

List of services and functions-Data Extraction

• Select data sources and determine the types of filters to be applied to individual sources

• Generate automatic extract files from operational systems using replication and other techniques

• Create intermediary files to store selected data to be merged later• Transport extracted files from multiple platforms• Provide automated job control services for creating extract files• Reformat input from outside sources• Reformat input from departmental data files, databases, and

spreadsheets• Generate common application code for data extraction• Resolve inconsistencies for common data elements from multiple

sources

List of services and functions-Data Transformation

• Map input data to data for data warehouse repository• Clean data, deduplicate, and merge/purge• Denormalize extracted data structures as required by

the dimensional model of the data warehouse• Convert data types• Calculate and derive attribute values• Check for referential integrity• Aggregate data as needed• Resolve missing values• Consolidate and integrate data

List of functions and services-Data staging

• Provide backup and recovery for staging area repositories• Sort and merge files• Create files as input to make changes to dimension tables• If data staging storage is a relational database, create and

populate database• Preserve audit trail to relate each data item in the data

warehouse to input source• Resolve and create primary and foreign keys for load tables• Consolidate datasets and create flat files for loading through

DBMS utilities• If staging area storage is a relational database, extract load files

Data Storage

• loading the data from the staging area into the data warehouse repository

• before loading data into the data ware the metadata repository gets populated

• For top-bottom approach there could be movements of data from the enterprise-wide data warehouse repository to the repositories of the dependent data marts

• For bottom-up approach data movements stop with the appropriate conformed data marts

Information Delivery

• Information access in a data warehouse is through online queries and interactive analysis sessions

• data warehouse will also be producing regular and ad hoc reports.

• data warehouse feeds data to proprietary multidimensional databases (MDDBs) where summarized data is kept as multidimensional cubes of information

Data stores for information delivery

Function and services• Provide security to control information access and monitor user access• Allow users to browse data warehouse content by hiding internal

complexities• Automatically reformat queries for optimal execution, from aggregate

tables as well• Provide self-service report generation for users, consisting of a variety of

flexible options to create, schedule, and run reports• Store result sets of queries and reports for future use• Provide multiple levels of data granularity• Provide event triggers to monitor data loading• Make provision for the users to perform complex analysis through OLAP• Enable data feeds to downstream, specialized decisions support systems

such as EIS and data mining

Summing up……

• Architecture is the structure that brings all the components together.

• The architectural components support the functioning of the data warehouse in the three major areas of data acquisition, data storage, and information delivery.

Infrastructure of DW

G.Anuradha

InfrastructureElements that enable the architecture to be

implemented.Operational – help to keep the DW going

People Procedures Training Management software

Physical Hardware components Operating system Network, network software

Features of Hardware & OSHardware

ScalabilityVendor supportVendor stability

OSScalabilitySecurityReliabilityAvailabilityPreemptive multitaskingMemory protection

Possible optionsMainframes

Old hardwareDesigned for OLTPExpensiveNot easily scalable

Open System ServersUNIX servers are most optedRobustAdapted for parallel processing

NT ServersMedium-sized data warehousesLimited parallel processingCost effective for small or medium DW

Platform OptionsA computing platform is the set hardware

components, operating system, network & network software.

Both Online Transaction Processing and Decision Support Systems need a computing platform.

Single Platform OptionAll functions from back-end data extraction to

front-end query processing is performed on one platform.Data flows smoothly, no conversions requiredNo middleware requiredLimitationsLegacy platform stretched to capacityNon-availability of toolsMultiple legacy platformsCompany’s migration policy

Hybrid Platform OptionEliminate s the drawbacks of single platform

optionData extraction: Each source is extracted on its

own computing platformInitial reformatting & merging: The extracted

file from each source is reformatted & merged, on their respective platforms

Preliminary data cleansing: Verify extracted data for missing values & data types.

Transformation & Consolidation: Performed on the platform where the staging area resides.

Validation & Final Quality CheckCreation of Load Images

Options for staging areaLegacy platforms – when all data sources are

on the same platform, we can create a DW also on the same

Data storage platform – the warehouse DBMS runs here. This can be used for staging also.

Separate optimal platform – a separate platform for staging data

Server HardwareServer hardware is most important

ScalabilityQuery processing

Data movement options

Client/Server architecture for DW

Considerations on client workstationsDepends on type of users

casual user-Web browser and HTML reportsAnalyst-more powerful workstation machine

Practically feasible solution is a minimum configuration on an appropriate platform that would support a standard set of information delivery tools in DW

Platform options as DW matures

Parallel processingSymmetric multiprocessingClustersMassively parallel processingCache-coherent Nonuniform Memory

Architecture

Symmetric Multiprocessing

Clusters

Massively Parallel Processing

NUMA or ccNUMA

Database Software

Many operations can be parallelizedmass loading of data, full table scans, queries

with exclusion conditions, queries with grouping, selection with distinct values, aggregation, sorting, creation of tables using subqueries, creating and rebuilding indexes, inserting rows into a table from other tables, enabling constraints, star transformation

Types of parallelization

Software Tools

Summing upInfrastructure acts as the foundation

supporting the data warehouse architectureData warehouse infrastructure consists of

operational infrastructure and physical infrastructure.

Hardware and operating systems make up the computing environment for the DW.

Several options exist for the computing platforms needed to implement the various architectural components.

Summing upSelecting the server hardware is a key

decision. Invariably, the choice is one of the four parallel server architectures.

Current database software products are able to perform interquery and intraquery parallelization.

Software tools are used in the data warehouse for data modeling, data extraction, data transformation, data loading, data quality assurance, queries and reports, and online analytical processing (OLAP).

Tools are also used as middleware, alert systems,

and for data warehouse administration.

METADATAData dictionary or data catalogContains data about the data in the DW like

data structuresfiles and addressesindexes

Types of MetadataOperationalExtraction & TransformationalEnd-User

Need for a MetadataFor using the DWFor building the DWFor administering the DWAutomation of the DW

Metadata by functional areasEvery DW process occurs in one of these 3

areasData acquisitionData storageInformation delivery

Data acquisition - metadata

Information Delivery – metadata

Types of MetadataBusiness metadata

Portrays DW from the end user perspectiveShows business names, not actual file namesLess structured as compared to technical

metadataUsed by business analysts and other end users.

Technical metadataShows the actual structure and content of the

DWActs as a guide to build, maintain and

administer the DWUsed the the data warehouse administrator,

and other IT staff working on the DW.

How to provide metadataMetadata requirementsSourcesChallengesRepositoryIntegration and standardsImplementation options