Massive But Agile: Best Practices For Scaling The Next-Generation Enterprise Data Warehouse
June 3, 2009
Why Read This Report
Information and knowledge management (I&KM) professionals continue to expand the scale, scope, and deployment roles for their enterprise data warehouse (EDW) investments. Today's most demanding EDW environments support petabytes of aggregated data, trillions of records, thousands of concurrent users and queries, complex mixed-query workloads, subsecond latencies, and continuous, high-volume data loading. Information managers are adopting EDW best practices that push the scalability and performance envelope without sacrificing the agility to optimize this critical infrastructure to ever-changing analytic workloads. Some key best practices involve deploying 64-bit multicore EDW processing nodes, scaling out through shared-nothing massively parallel processing (MPP), pushing query processing to grid-enabled intelligent storage layers, applying efficient compression in the storage layer, and deploying preconfigured high-end EDW appliances.
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Table of Contents
- Information Tsunami: Putting Pressure On The Enterprise Data Warehouse
- Best Practices: EDW Scaling, Acceleration, And Performance Optimization
- EDW Scaling Best Practice No. 1: Scale Your EDW Through Parallelism
- EDW Scaling Best Practice No. 2: Accelerate Your EDW With Appliances
- EDW Scaling Best Practice No. 3: Optimize Your EDW's Distributed Storage Layer
- EDW Scaling Best Practice No. 4: Retune And Rebalance Workloads
- Forrester's EDW Scaling Next Practices
- Case Studies
- Supplemental Material
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