Why enterprise data warehouse offloading?

The explosive growth of data has forced organizations to use their enterprise data warehouse (EDW) for purposes that it was never intended for — including running extract, transform, load (ETL) workloads and storing large volumes of unused data. New types of data, updated analytics practices and more efficient, cost-effective methods of storing and accessing data have put an additional strain on EDW infrastructures.

One of the most effective modernization approaches is offloading EDW data and ETL workloads to an Apache Hadoop data lake, reducing cost and EDW performance strain.

IBM's complete, proven solution for EDW supports data movement, quality, governance and replication. It provides a scalable, high-performance platform that enables you to leverage your team's existing skills and data integration job assets while realizing all the benefits of data offloading.

-> Read why it’s time to optimize your enterprise data warehouse (PDF, 94.9 KB)

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IBM has an unmatched modular solution for data warehouse offloading


Extract, move and ingest massive amounts of data with a shared-nothing, parallel platform without limiting your performance.

Transform and integrate

Build a job once and run it in the enterprise data warehouse, in the extract, transform, load (ETL) grid and in Hadoop without modification, using existing developer skills and ETL assets.

Improve data quality

Eliminate ”garbage in, garbage out” analytics and reporting by implementing comprehensive, fast and scalable data quality processing.

Govern your data

Keep your data lake from becoming a data swamp by implementing comprehensive data governance, including end-to-end data lineage for all your business users.


Optimize resource use and deliver data where and when it's needed, with reduced latency and on-time updating.

Augment and enrich

Prepare vast amounts of structured and unstructured data for enriched analytics, machine learning and artificial intelligence.

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