From Redshift, EMR and Glue to Databricks on AWS
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 →Short answer
To leave Hadoop for Databricks, inventory the cluster, retire unused data, move Spark jobs with small changes, rewrite MapReduce and Oozie as declarative pipelines and Lakeflow Jobs, map Ranger or Sentry policies to Unity Catalog, and reconcile before switching users.
Key takeaways
- Spark moves easily; MapReduce and Oozie need rewriting.
- Ranger and Sentry map to Unity Catalog.
- Move high-value workloads first.
Many enterprises still run important workloads on Hadoop. The clusters are often near end of support, upgrades are risky, and the engineers who built them have moved on.
Why move now
- Hardware and support renewals keep coming.
- Security policies live in Ranger or Sentry, separate from everything else.
- Adding modern AI on Hadoop is slow and fragile.
What moves easily
Spark jobs usually move with small changes. Hive tables convert to Delta tables. HDFS data copies to cloud object storage.
What needs more care
MapReduce jobs, Oozie workflows and custom scripts need rewriting. We turn them into declarative pipelines and Lakeflow Jobs, which are easier to test and monitor.
Security and governance
We map Ranger or Sentry policies to Unity Catalog, then add lineage and audit that Hadoop never had.
Our advice
Do not lift everything. Retire unused data and jobs first. Move the most valuable workloads early, so the business sees benefits before the last cluster is switched off.
About the author
NeuralShore Engineering, Databricks delivery team
Databricks architects, data engineers and AI engineers at NeuralShore, DLytica’s Databricks delivery company. Leadership comes from IBM data and AI architecture.
