Data Warehouse Offload to Hadoop

Free up Data Warehouse capacity and budget

Shift data and workloads to Hadoop

The True Cost of ELT

Today’s business world is demanding more from the data warehouse, because more than ever an organisation’s survival depends on its ability to transform data into actionable insights.

However, ELT data integration workloads are now consuming up to 80% of database capacity, resulting in:

  • Rising infrastructure costs
  • Increasing batch windows
  • Longer development cycles
  • Slower user query performance

Start Building Your Enterprise Data Hub

Shift Data and ELT Workloads to Hadoop

Syncsort's Data Warehouse Offload solution makes it easy to turn Hadoop into an ideal staging area for all your data – from structured to unstructured – a massively scalable location where you collect, prepare, blend, transform data – and then distribute it to the rest of the organisation.

By effectively offloading data and ELT workloads from the data warehouse into Hadoop or Apache Spark, you can:

  • Significantly reduce batch windows
  • Keep data readily available as long as you need
  • Free up significant data warehouse capacity
  • Reduce warehouse costs

Get the Ultimate Checklist for Hadoop ETL

Learn What to Do & What Not to Do

Looking for some expert advice to help get you started with offloading your Enterprise Data Warehouse to Hadoop?

Use this easy-to-follow checklist covering the three biggest challenges of using Hadoop as your main ETL tool – learn how you too can free up data warehouse capacity and avoid costly upgrades.

  1. Identify
  2. Offload
  3. Optimize & Secure

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