Large-scale data migration with Apache Spark
We re-engineered a fragile, manual data migration into a repeatable Apache Spark pipeline — moving massive datasets between stores with validation, resumability and zero data loss.
The challenge
Large-scale migrations done by hand are slow, error-prone and impossible to retry safely. A failure mid-run can corrupt or silently lose data, and there's rarely a way to verify the result.
A Spark-based pipeline
We built the migration as Apache Spark jobs with custom, schema-aware conversion, so transformation runs in parallel at scale across many service schemas instead of one record at a time.
Safe by construction
The pipeline is idempotent and resumable — a failed run picks up where it stopped — with row-level and aggregate validation comparing source and target so nothing is dropped unnoticed. Schema changes are version-controlled with Liquibase.
Cloud-native delivery
The whole pipeline is packaged and deployed on Kubernetes (with ArgoCD and Helm), so migration runs are reproducible and observable — not a one-off script on an engineer's laptop.
Outcomes
- ~80% faster migration than the prior manual process (internal benchmark).
- Zero data-loss incidents across the migration.
- A repeatable, observable pipeline that can be re-run safely.
Figures are client-reported or from internal benchmarks and are illustrative of typical results, not independently audited.
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