This is neither an "Ultimate List of Data Engineering Books" nor the "Data Engineering Must-Read List", but rather my subjective recommendation for a minimal data engineering library; particularly for data engineers coming from software development, as I used to.
General
- Reis, Housley: "Fundamentals of Data Engineering"; Oreilly Media, 2022.
- Discusses all aspects of data engineering and interlinks topics through "under currents".
Data Modeling
- Hoberman: "Data Modeling Made Simple"; Technics Publication, 2016.
- Must-read for anybody doing data modeling.
- Serra: "Deciphering Data Architecture"; Oreilly Media, 2024.
- Description and comparison of major data architectures.
Databases
- Forta: "SQL in 10 Minutes"; Pearson, 2010.
- The best minimal SQL guide I found, yet.
- Kaufmann, Meier: "SQL and NoSQL Databases"; Springer, 2019.
- Beyond touching all the database basics, the relational (SQL), document (Mongo) and graph model (Cypher) are recurringly contrasted.
Containerization
- Öggl, Kofler: "Docker"; Rheinwerk Computing, 2023.
- Touches on all facets of Docker alongside with typical application examples.
- Gkatziouras: "A Developer's Essential Guide to Docker Compose"; Packt, 2022.
- In deep description of the Compose orchestrator.
Extra
- Densmore: "Data Pipelines Pocket Reference"; Oreilly Media, 2021.
- Good example-driven overview; original source of EtLT (extract, partial transform, load, transform).
- Chromatic: "Extreme Programming Pocket Guide"; Oreilly Media, 2003.
- Extreme programming (XP) is the sanest agile method with some good sustainability practices like the 40h week.
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