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IBM Information Server

IBM Information Server. Transform – DataStage . Why “Transform?”. Why Transformation?. Business Driver: Single View of Corporate Data Projects Related to Information Infrastructure Application integration Platform migration On-demand transformation and correction

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IBM Information Server

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  1. IBM Information Server Transform – DataStage

  2. Why “Transform?”

  3. Why Transformation? • Business Driver: Single View of Corporate Data • Projects Related to Information Infrastructure • Application integration • Platform migration • On-demand transformation and correction • Application re-engineering and migration (ERP to CRM) • Decision Support (BI, DW, Data Marts) • Opportunity (discover new revenue sources) • Control (Fraud detection, inventory) • Regulatory compliance -SOX, BASEL, Money Laundering • Portals • Balanced scorecard dashboards, BAM Business Goals IT Initiatives Information Integration

  4. Transformation Pain • Multiple sources for the same entity • Lack of standards or consistent semantic meanings across systems • Embedded business intelligence • Evolving transformation requirements • Need for batch and real-time and service oriented architectures • Extreme data volumes! • Business rules for resolving data conflicts • Ownership and accountability • Zero re-use of skills and processes

  5. How Is This Being Done Today? • Hand coding: Java, C, C++, VB, .NET, COBOL, 4GLs… • Spreadsheet “farms” • Early generation ETL tools • Competitive products

  6. IBM Information ServerDelivering information you can trust Support for Service-Oriented Architectures Transform Deliver Understand Cleanse Discover, model, and govern information structure and content Standardize, merge, and correct information Combine and restructure information for new uses Synchronize, virtualize and move information for in-line delivery Platform Services Parallel Processing Administration Deployment Connectivity Metadata

  7. The IBM Solution: IBM Information ServerDelivering information you can trust IBM Information Server Unified Deployment Transform Deliver Understand Cleanse Unified Metadata Management WebSphere DataStage Complex transformation for simplified data exchange and reduced coding Parallel Processing Rich Connectivity to Applications, Data, and Content

  8. Implementation Examples • Uses real-time data in a financial data warehouse for intra-day analytics • Improves supply chain management by creating forecasts from POS data. • Basel II initiative will release about 40% of its minimum capital requirements • Replaced 4,000 hand-coded interfaces to create single view of ticket data • Manages 3 terabytes of store sales data for customer and product analysis Deutsche Bahn Group

  9. COMMON SERVICES PARALLEL PROCESSING METADATA COMMON CONNECTIVITY WebSphere DataStage • Design integration projects within a graphic, codeless environment • Integrate data from the widest range of enterprise and external data sources • Produce re-useable components • Deploy jobs in real-time, batch mode, or as services • Leverage the most scalable and adaptable parallel processing engine IBM Information Server DATASTAGE QUALITYSTAGE CLIENT Sources Targets

  10. Graphical Design Metaphor

  11. Pre-Built Transformations for Productivity

  12. Graphical Design Metaphor Extensive list of availabletransformation functionsto select from: Context-sensitive menu: Easy access to transforms 12

  13. Error notification Immediate notification whenthere’s a problem! 13

  14. Extensive Re-use • Shared Containers • Graphical unit of re-use • Share one developer’s (subject matter expert) • Meta data research • Business rule definitions • Transformation logic • Special techniques • Routines • Re-usable functions • Web Services • Deploy jobs as web services. Invoke from other jobs or applications • Use Web Services

  15. Connectivity Ensures Data Access Enterprise Applications JD Edwards Oracle Applications PeopleSoft SAP BW (BAPI, IDOC) SAP R/3 (ABAP, BAPI, IDOC) Siebel RDBMS IBM DB2 IBM IMS VSAM Oracle Informix RedBrick SQL Server Sybase Teradata U2 (Universe, UniData) Tandem NON-STOP SQL SAS Business Exchange Formats XMLS EXML EDI FIX SWIFT HIPAA Real-Time WebSphere MQ SeeBeyond Java Messaging Services Java (Client & Transformer) XML (Read / Write) XSL-T XSL-T Transformer Web Services (SOAP) Enterprise Java Beans Flat File and General Access VSAM VSAM CICS IDMS C-ISAM Sequential File Complex Flat File File Set Data Set Named Pipe FTP (standard, secure) Compressed / Encoded Data External Command Call Parallel Wrap 3rd party applications …And many more!

  16. Benefits of Scalability - or - Process the same data volume in less time Process more data in the same amount of time 20 15 10 5 1 t 750 500 250 Processing Volume (gigabytes) Processing Time (hours) 2 4 8 12 16 24 32 - - - 2 4 8 12 16 24 32 - - - Number of CPUs Number of CPUs

  17. Parallel Execution Enables Timely Integration MPP, GRID, and Clustered Systems Uniprocessor SMP System

  18. Enabling Parallelism Given a Job Design: …DataStage creates “n” processes at runtimefor each Stage, where “n” is the number of logical nodes defined in a configuration file 18

  19. Metadata Driven Integration • Shared metadata across product modules • Better and faster communication between team members • Immediate access to definitions and notes on all objects • Greater understanding, better data • Powerful Metadata driven design tools • Quick Find and Advanced Find • Impact Analysis • Data Lineage reports • Greater productivity, easier maintenance, reuse Impact Analysis Find Capability

  20. DataStage Strength Summary • Graphical, top-down design metaphor • Extensible, component based architecture • Strong Re-use capabilities • Shared Containers, Routines & Web Services • Graphical sequencing (“job flow”) • Application Deployment • Parameterization • Changed Data Capture • Ubiquitous Connectivity • Unlimited Scalability • Design serially, deploy in parallel

  21. Thank You

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