Big data use case: Offloading the data warehouse to Hadoop

The true cost of ELT

true cost of ELTToday’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

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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

Originally written by Keylink Technologies, Australia

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