Enterprise Data Warehouse Optimization

Enterprise Data Warehouse Optimization

EDW offloading represents a key step in a larger objective to modernize the enterprise analytics architecture.

Published By - WisdomPlexus

The enterprise data warehouse (EDW) has been at the cornerstone of enterprise data strategies for over 20 years. EDW systems have traditionally been built on relatively costly hardware infrastructures. But ever-growing data volume and increasingly complex processing have raised the cost of EDW software and hardware licenses while impacting the performance needed for analytic insights. Organizations can now use EDW offloading and optimization techniques to reduce costs of storing, processing and analyzing large volumes of data.

Getting data governance right is critical to your business success. That means ensuring your data is clean, of excellent quality and of verifiable lineage. Such governance principles can be applied in hadoop-like environments. Hadoop is designed to store, process and analyze large volumes of data at significantly lower cost than a data warehouse. But to get the return on investment, you must infuse data governance processes as part of offloading.

When in place, these below mentioned capabilities help maximize the return from enterprise data warehouse offloading projects. Some of these requirements focus on moving and transforming data while others involve a people-process architecture:

  1. Move data
  2. Transform and integrate
  3. Improve data quality
  4. Govern your data
  5. Replicate
  6. Augment and enrich
  7. Reference architecture
  8. Implementation patterns

Extracting, moving and ingesting large amounts of data from the data warehouse to Hadoop requires a shared nothing, massively parallel platform with no limitation on throughput and performance. Your organization needs a fully scalable data integration platform that supports extraction, movement and ingestion with an easy-to-use drag and drop interface. You can insert different levels of parallelism in different phases of the process, depending on your own requirements.

In many traditional data warehouse environments, organizations aren’t able to implement data quality processing. Many organizations use the EDW offloading process to eliminate garbage-in, garbage-out reporting and analytics by implementing comprehensive and scalable data quality processing. If you don’t put high-quality data into the hadoop infrastructure, the resulting analytics are of limited value.

Typically, EDW offloading represents a key step in a larger objective to modernize the enterprise analytics architecture. But all require a similar foundation to carry out their mission. IBM has introduced a proven, flexible reference architecture that reduces the risks, costs and time required to modernize projects.

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