Business Impact

  • 73%

    reduction in query cost with optimized scripts

  • 70%

    improvement in performance with serially optimized queries on BigQuery

  • 90%

    improvement in performance with optimized query orchestration

Customer Key Facts

  • Country : USA
  • Industry : Retail

Problem Context

The client is a multinational lifestyle retail corporation with a diverse brand portfolio. They wanted a specific subset of data to be transferred from Netezza to BQ, followed by the optimization of complex Netezza jobs on BigQuery. The client wanted to know if customers were classified as “New,” “Active,” “Re-Activated,” or “Unmatched” at different levels, including DAY, WEEK, MONTH, QUARTER, and YEAR.


  • Gigabytes of data stored in on-prem Netezza boxes in the form of tables
  • Long run times (3 hours+) for business critical queries (80+ queries)

Technologies Used

Google BigQuery
Google Cloud Composer

Comparing the performance of running queries within Netezza and BigQuery


Quantiphi helped the client to compare the performance of running queries within Netezza and BigQuery by translating and optimizing existing Netezza jobs on BigQuery specific to the NAR (new active re-active) use case.


34% reduction in the total number of queries by combining smaller query actions with larger ones

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