Example journey. This page shows how we run this type of migration. It is not a named client result. References from comparable projects are available on request.
This example journey shows how a fintech on AWS would consolidate Redshift, EMR and Glue into Databricks in its own AWS account: data stays in S3, ETL and Spark move into pipelines and jobs, Redshift SQL is converted, and fraud monitoring moves to real time.
| Type | Example migration journey |
|---|---|
| Industry | Fintech |
| Migration | AWS data services |
| Technologies | Databricks SQL, Lakeflow Jobs, Real-time pipelines, Unity Catalog |
Starting point
A fintech runs reporting on Redshift, Spark jobs on EMR and ETL on Glue. Each service works, but together they are hard to govern, and fraud analytics lag behind transactions.
How we would approach it
- Stay on AWS: deploy Databricks in the existing AWS account, with tables in the company's own S3.
- Consolidate: move Glue ETL and EMR jobs into declarative pipelines and Lakeflow Jobs.
- Convert: convert Redshift SQL and validate dashboards.
- Go real time: rebuild fraud monitoring with real-time pipelines on the same platform.
Value the fintech should expect
- Fewer services to run and pay for.
- One governance model for analytics, ML and agents.
- Fraud signals closer to the moment of payment.
What to measure
Monthly AWS data spend, pipeline count and failures, fraud alert latency, and analyst time to answer.
Read the full Redshift, EMR and Glue to Databricks on AWS guide →
