Redshift, EMR, Glue, Athena
Moving from Redshift, EMR and Glue to Databricks on AWS consolidates several data services into one governed lakehouse. Your data stays in your own S3 in open Delta and Iceberg tables, and ETL, SQL, ML and agents run on one platform under Unity Catalog.
Redshift schemas and SQL, EMR Spark jobs, Glue catalogs and ETL, Athena queries
Each component has a clear target on the Databricks Data Intelligence Platform.
| AWS data services | Databricks |
|---|---|
| Amazon Redshift | Databricks SQL serverless warehouses |
| Amazon EMR (Spark) | Lakeflow Jobs on serverless compute |
| AWS Glue ETL | Lakeflow Declarative Pipelines |
| AWS Glue Data Catalog | Unity Catalog (with federation during transition) |
| Amazon Athena | Databricks SQL |
| Step Functions or MWAA | Lakeflow Jobs |
| Amazon S3 | Stays in place, holding Delta and Iceberg tables |
Inventory every table, job, report and user. Map dependencies, find what is unused, and estimate effort and cost.
Output: Migration assessment and business caseDesign the target lakehouse, governance and security. Group workloads into waves by value and risk.
Output: Target architecture and wave planConvert SQL and ETL with LakeBridge and our accelerators, then review and test the output.
Output: Converted, tested code per waveRun old and new side by side and compare results table by table until they match.
Output: Signed-off reconciliation reportsMove users in waves, retire the old system, then tune cost and performance.
Output: Decommission plan and FinOps baselineWe use Databricks LakeBridge for profiling, code conversion and reconciliation, alongside our own accelerators and engineer review of every critical path.
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 →You do not have to leave AWS to simplify your data platform. Consolidate several services into one governed lakehouse.
Read the article: From Redshift, EMR and Glue to Databricks on AWS →No. Databricks runs inside your AWS account and keeps data in your own S3 buckets.
Much of it can, using LakeBridge and our accelerators. Engineers then review and test the converted code, and every report is reconciled before users switch.
