"lakeFS: Version Control for Data Lakes—Branch, Merge, and Reproduce"
Modern data lakes promise scale and flexibility, yet too often deliver fragile pipelines, irreproducible results, and risky "promotions" performed by copying files between buckets. This book targets experienced data engineers, platform teams, and ML infrastructure practitioners who need Git-like control over object storage—without replacing their lake. You'll learn to treat datasets as first-class, versioned artifacts and to run parallel development safely in production-grade environments.
You'll build a rigorous mental model of lakeFS as a control plane: repositories, references (branches and tags), versioned views of objects, and the commit DAG that encodes lineage. From there, the book goes deep on zero-copy branching, uncommitted changes and atomic commits, diff-at-scale for review and quality gates, and three-way merges with conflict taxonomy and recovery playbooks. You'll leave able to design repeatable operational flows—branch-per-job pipelines, validate-then-merge promotion, and tag-based releases—backed by automation hooks, robust clients (lakectl and APIs), and S3-compatible integration patterns.
Expect an advanced, systems-oriented treatment: correctness guarantees, performance trade-offs, tool pitfalls, failure modes, and production governance. Readers should be comfortable with object storage, distributed compute (e.g., Spark/Hadoop), and CI/CD-style automation; the focus is on precise semantics, decision criteria, and running lakeFS as a dependable platform.
