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Data Engineering

Build reliable data systems that turn raw events into useful, trustworthy datasets and products.

Learning paths

  • Communication — API contracts and evolution, real-time delivery, and traffic routing (load balancers, CDN, DNS).
  • Concurrency, Async & Parallel — coordinate work safely and use compute efficiently.
  • Databases — model, operate, scale, and tune data stores.
  • Distributed Systems — reason about coordination, transactions, reliability, and trade-offs.
  • Event Streaming — design queues, event-driven systems, and stream processing.
  • Scheduled Jobs — orchestrate durable, idempotent batch workflows.
  • Storage Systems — choose files, object stores, table formats, query engines, and compute tools.

How to use this section

  1. Start with the path closest to the system you are building or operating.
  2. Follow its guides from foundations to production trade-offs.
  3. Apply the checklists and exercises to a real pipeline or dataset.
  4. Use the next path when a dependency or design decision requires it.