Financial services · Databricks
Block: Expanding access to the economy
Public case study · not a Metric Oak engagement
- Reduction in compute cost
- 12X
- Reduction in data egress cost
- 20%
- Of data managed and governed
- 12PB
What happened
The story in brief
The fintech group behind Square and Cash App relies on machine learning to spot fraud across millions of transactions. It adopted declarative pipelines on Databricks to simplify how its streaming and batch data is built and maintained, speeding up delivery for its data teams.
How it compares
- 20% reduction in data egress cost, against a median reported cost reduction of 60% across 19 financial services results.
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Related insights
Frequently asked questions
What results did Block report?
Block reported 12X reduction in compute cost; 20% reduction in data egress cost; 12PB of data managed and governed, according to its customer story published by Databricks.
Which platform and technologies did Block use?
Block used Databricks, including Unity Catalog, Declarative pipelines, Delta Sharing, Apache Spark, Secure Data Sharing, Large language models.
How do these results compare with other financial services programs?
20% reduction in data egress cost, against a median reported cost reduction of 60% across 19 financial services results.
Source
Customer story published by Databricks: Expanding access to the economy (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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