Fundamentals Of Data Engineering By Joe Reis Pdf →

Reis argues that the term "Data Warehouse" is a logical concept, not a physical one. The PDF explains the shift toward the Lakehouse (using tools like Delta Lake or Iceberg). It argues that separating storage (S3/GCS) from compute (Snowflake/Redshift/Spark) is the fundamental shift of the 2020s.

| Book | Focus | |------|-------| | Fundamentals of Data Engineering (Reis & Housley) | Lifecycle, architecture, decision frameworks | | Designing Data-Intensive Applications (Kleppmann) | Distributed systems theory (more advanced) | | Data Engineering with dbt (TBD) | Practical transformation coding | | The Data Warehouse Toolkit (Kimball) | Dimensional modeling (classic, narrow focus) | Fundamentals of Data Engineering by Joe Reis PDF

The authors replace the outdated “ETL/ELT pipeline” mental model with the Data Engineering Lifecycle: Reis argues that the term "Data Warehouse" is

Why this matters: It forces you to consider all stages, not just the pipeline. For example, many failures come from misunderstanding source systems (Generation) or forgetting that serving data for a dashboard is different from serving for an ML model. Why this matters: It forces you to consider

For years, data engineering was ingress-only. Reis was early to champion Reverse ETL (taking data from the warehouse and pushing it back to Salesforce, Marketo, or a CRM). The PDF details why this closes the loop and turns data into an operational asset.

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