This powerpoint slide deck is the presentation given at the Microsoft center in Waltham, MA titled Leading Practices and Insights for Managing Data Integration Initiatives.
Topics covered include:
Key Drivers
Approaches and Strategy
Tools and Products
Useful Case Studies
Success Factors
5. Data Integration Overview 03/09/11 Definition - combining data residing in different sources and providing users with a unified view of these data Mediated Schema Example Data Warehouse Example
39. 03/09/11 ETL Vendors ETL Vendors ETL Tools Microsoft SQL Server Integration Services Oracle Oracle Warehouse Builder (OWB) SAP Business Objects Data Integrator & Data Services IBM IBM Information Server (Datastage) IBM Data Manager/Decision Stream (Cognos) SAS Institute SAS Data Integration Studio Informatica PowerCenter Ab Initio Co>Operating System Information Builders Data Migrator Adeptia Adeptia Integration Server CastIron Systems OmniConnect Platform Pitney Bowes Business Insight DataFlow Manager Pervasive Data Integrator Elixir Elixir Repertoire Javlin Clover ETL Pentaho Pentaho Data Integration Talend Talend Open Studio
40. 03/09/11 ETL / EAI - Tool Strengths ETL EAI Excels at bulk data movement Limited in data movement capabilities Provides for complex transformations, aggregation from multiple sources and sophisticated business rules. Offer less sophisticated transformation and extraction functions Assumes data delays. Operates in real time Are batch-oriented, making them fast and simple for one-time projects and testing Work better with continuously interacting systems Offers little in the way of workflow Workflow-oriented at the core Works primarily at the session layer Works primarily at the transport layer
Editor's Notes
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Bulk Extract – utilizes copy management tools or unload utilities to extract all or a subset of the operational relational database. The data which has been extracted may be transformed to the format used by the target on the host or target server . The DBMS system load tools are then used in order to refresh the database target. File Compare – process compares the newly extracted operational data to the previous version. After that, a set of incremental change records is created and are applied as updates to the target server within the scheduled process. Change Data Propagation – captures and records the changes to the file as part of the application change process. Techniques that can be used include triggers, log exits, log post processing or DBMS extensions. A file of incremental changes is created to contain the captured changes.
Data stewardship involves taking responsibility for data elements for their end-to-end usage across the enterprise .
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