Financial services · Databricks
AXA Japan unifies data and modernizes analytics with Databricks
Public case study · not a Metric Oak engagement
- Reduction in warehouse costs for certain phases of the migration
- 60%
- Lower ETL costs in migrated workloads
- 70%
- Faster development after retiring legacy systems and modernizing workflows
- 40%
What happened
The story in brief
A Japanese life and P&C insurer with a highly data-literate workforce was outgrowing its legacy warehouse and ETL. It migrated workloads to Databricks in phases, retiring legacy systems along the way and cutting both warehouse and ETL costs while speeding up development.
How it compares
- 60% reduction in warehouse costs for certain phases of the migration, against a median reported cost reduction of 60% across 19 financial services results.
- 70% lower ETL costs in migrated workloads, against a median reported cost reduction of 60% across 19 financial services results.
Planning something similar?
Cloud data migration
Legacy warehouse to lakehouse, with parallel-run validation. Senior engineers, fixed scope, measured results.
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Related insights
Frequently asked questions
What results did AXA Japan report?
AXA Japan reported 60% reduction in warehouse costs for certain phases of the migration; 70% lower ETL costs in migrated workloads; 40% faster development after retiring legacy systems and modernizing workflows, according to its customer story published by Databricks.
Which platform and technologies did AXA Japan use?
AXA Japan used Databricks, including Unity Catalog, Databricks SQL, dbt.
How do these results compare with other financial services programs?
60% reduction in warehouse costs for certain phases of the migration, against a median reported cost reduction of 60% across 19 financial services results. 70% lower ETL costs in migrated workloads, against a median reported cost reduction of 60% across 19 financial services results.
Source
Customer story published by Databricks: AXA Japan unifies data and modernizes analytics with Databricks (opens databricks.com in a new tab) on databricks.com. Figures as published, retrieved 27 October 2026. The summary above is Metric Oak's own. This is not a Metric Oak engagement.
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