Applying DevOps principles (automation, CI/CD) to data pipelines.
The lifecycle model is re-stated in multiple chapters. It’s useful for reinforcement but feels padded in the second half. Fundamentals of Data Engineering by Joe Reis PDF
Data is produced by systems, users, and applications. Data engineers must understand where this data originates to ensure its quality. II. Storage Applying DevOps principles (automation
A data pipeline is only successful if it solves a tangible business problem. Data engineers must communicate effectively with non-technical stakeholders. or AWS. Tools change rapidly
Many technical books focus heavily on specific tools like Apache Spark, Snowflake, or AWS. Tools change rapidly, but foundational architectures endure. Joe Reis and Matt Housley address this by delivering a of the data engineering landscape.
Applying DevOps principles (automation, CI/CD) to data pipelines.
The lifecycle model is re-stated in multiple chapters. It’s useful for reinforcement but feels padded in the second half.
Data is produced by systems, users, and applications. Data engineers must understand where this data originates to ensure its quality. II. Storage
A data pipeline is only successful if it solves a tangible business problem. Data engineers must communicate effectively with non-technical stakeholders.
Many technical books focus heavily on specific tools like Apache Spark, Snowflake, or AWS. Tools change rapidly, but foundational architectures endure. Joe Reis and Matt Housley address this by delivering a of the data engineering landscape.