Subjects / Data engineering
Best books to learn Data engineering, in order
Data engineering is really distributed systems wearing a data hat, so a reading order that skips the fundamentals leaves you cargo-culting pipelines. The arc runs from data modeling and SQL, through batch and streaming processing and the storage-and-orchestration stack, into the distributed-systems trade-offs (consistency, partitioning, fault tolerance) that explain why the tools behave the way they do.
Reading paths for data engineering
Data engineering: books for building reliable data pipelines
Intermediate9books69 hrs4 stages
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Frequently asked questions
- How should I approach learning data engineering?
- Data engineering is really distributed systems wearing a data hat, so a reading order that skips the fundamentals leaves you cargo-culting pipelines. The arc runs from data modeling and SQL, through batch and streaming processing and the storage-and-orchestration stack, into the distributed-systems trade-offs (consistency, partitioning, fault tolerance) that explain why the tools behave the way they do.
- What's a good book to start data engineering with?
- A strong starting point is The data warehouse toolkit by Ralph Kimball. The ordered reading paths above show exactly where it fits and what to read next.
- What should I read after data engineering?
- Once you have the fundamentals, explore closely related subjects like MLOps, Reinforcement learning, Quantum computing.