Fintechs use Databricks to analyse every payment event, monitor transactions for compliance and give product teams self-service answers.
Fintechs use Databricks for real-time payments analytics, transaction monitoring, product and growth analytics, and running operational and analytical work on one governed copy of data with Lakebase.
Event volumes outgrow the transactional database and reporting slows.
ZeroBus Ingest · Delta Lake · Lakehouse real-time warehouseLow-latency analytics on every payment event.
Small compliance teams cannot keep up with alert volume.
Real-time pipelines · MLflow · GeniePrioritized alerts with the context investigators need.
Funnel and retention questions take days of manual SQL.
Databricks SQL · AI/BI · Genie spacesSelf-service answers on activation, retention and revenue.
App data in Postgres must be copied before anyone can analyse it.
Lakebase · Unity Catalog · Databricks AppsOperational and analytical work on governed data without fragile copies.
Several AWS data services stitched together, with rising cost and slow fraud analytics.
Read the journey: AWS Redshift, EMR and Glue to Databricks for a fintech →Fast-growing fintechs outgrow their app database. A lakehouse with real-time pipelines keeps analytics and risk on one platform.
Read the article: Fintech: real-time payments and fraud without a second stack →
