From Apache Hadoop/Spark to Amazon EMR: Best Migration Practices and Cost Optimization Strategies
Migrate Big Data, Apache Spark, and Apache Hadoop workloads to Amazon EMR — and right-size the bill while you're at it.
On-premises Hadoop and Spark clusters scale poorly, demand constant ops attention, and tie capacity to capex cycles that no longer match how the business actually consumes data. Amazon EMR collapses the operational surface — managed clusters, separated storage and compute, elastic scaling — but only if the migration is planned around the workloads that actually run, not the cluster that runs them today.
This whitepaper walks through how Provectus approaches Hadoop/Spark-to-EMR migrations: the scenarios that matter, the risks worth mitigating up front, and the EMR-specific cost levers (instance fleets, Spot, EMRFS, auto-scaling) that turn a lift-and-shift into a meaningful TCO reduction.
What’s inside
- An overview of Amazon EMR — features, common use cases, and where it fits relative to other AWS analytics services
- Industry trends shaping how Hadoop/Spark workloads are run today, and why teams are leaving on-prem
- Business and technology benefits of moving to EMR (managed ops, decoupled storage/compute, elasticity)
- Top migration scenarios with the trade-offs of each approach (lift-and-shift vs. re-platform vs. re-architect)
- Migration risk mitigation strategies — data, dependencies, and operational continuity
- EMR-specific cost optimization best practices — Spot fleets, right-sizing, EMRFS, auto-scaling
For data platform, infrastructure, and analytics leaders running Hadoop or Spark on-prem (or on EC2) and weighing the move to EMR.