The data warehouse (DW) is still an effective tool for complex data analytics and it isn’t going anywhere. Not soon, anyway. But DWs are expensive. So why are you clogging your DW’s analytics bandwidth with less-valuable storage and ETL processing?
Migrating storage and large-scale or batch processing to Hadoop lets both the DW and Hadoop do what they do best. Hadoop’s parallel processing, scalable and cost-efficient platform allows enterprises to save on storage and processing costs. The DW, now with more available processing power, can be focused on business intelligence (BI) activities. A bonus: for savvy enterprises with an eye to the future, migrating to Hadoop sets them up to successfully exploit original raw data of all types for data exploration and new use cases.
Here are four good reasons for a DW offload to Hadoop:
1. Save millions in storage costs
Hadoop can store raw data in any format at a fraction of the cost of the DW. In fact, we helped one client achieve 20 times the storage capacity of their DW at 50% of the cost of a previously planned DW upgrade. Another client achieved a 100x cost reduction per terabyte of stored data.
2. Significantly speed up processing
Hadoop’s flexible architecture enables faster loading of data and parallel processing, resulting in faster time to insight. For example, one of our clients quadrupled the throughput of their system after migrating processing to Hadoop. Hadoop is also much more effective than the DW for processing the increasing amount of unstructured and semi-structured data that’s important for analytics today.
3. Maximize DW for BI
Costly DW resources shouldn’t be wasted on low-value activities such as data transformation. One of our clients realized that 90% of their DW platform was being used for ETL processes, leaving little processing power available for high-value analytics and business intelligence activities. A DW offload to Hadoop made it possible for the enterprise to use its assets more strategically.
4. Extract more value from all data
Lower cost means enterprises can store more data in an accessible format—in an “active archive” versus on tape. Extending data retention periods for historical data and eliminating time-consuming backup processes supports more in-depth trend analyses that can lead to further business insights and more effective business strategies.
The big picture: Hadoop beyond the EDW offload
Offloading your DW’s storage and processing to Hadoop is a good first step towards the future of where big data architecture is headed—and an offload will produce immediate and significant ROI. However, thinking more broadly, a DW offload also can serve as a launch pad to begin to consider what other ways a hybrid architecture could benefit your business. With cloud solutions you can further separate your compute and storage needs, allowing on-demand analytics that use compute power only when required. Could Hadoop become a strategic core component of an enterprise data hub? What infrastructure would you need to put in place to make it a reality? How would you manage metadata and data governance across multiple platforms? It’s exciting stuff—and we work with clients every day to make it happen. Please contact us if you’d like to know more.
About the AuthorFollow on Twitter Follow on Linkedin Visit Website More Content by Ben Sharma