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A Practical Guide to Data Warehouse Offload and Optimization with Hadoop

January 1, 2018

Data warehouses have been the foundation for business analytics for many years, and have grown to support increasing data volumes as well as analytics and ETL workloads. Yet over time, some data becomes older and is used infrequently or not at all. And ETL workloads implemented as transformations inside the data warehouse can grow to occupy significant CPU cycles, impacting resources that support critical analytics processes.

With Hadoop, enterprises have the opportunity to offload less valuable data from their data warehouse as well as some workloads like ETL, freeing up valuable resources in the data warehouse while reducing total cost of ownership.

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