If you’re aiming to build scalable and reliable data pipelines using Databricks or AWS, having the right learning resources is crucial. After reviewing several top-rated books, I’ve identified two must-read titles that provide hands-on guidance and real-world examples for modern data engineers.
📚 Top Books to Learn Data Pipeline Development in Databricks and AWS
If you’re aiming to build scalable and reliable data pipelines using Databricks or AWS, having the right learning resources is crucial. After reviewing several top-rated books, I’ve identified two must-read titles that provide hands-on guidance and real-world examples for modern data engineers.
1️⃣ Mastering Data Engineering and Analytics with Databricks
A Hands-on Guide to Build Scalable Pipelines Using Databricks, Delta Lake, and MLflow
This comprehensive guide stands out for its practical approach to building and optimizing data pipelines on the Databricks Lakehouse Platform. It combines foundational concepts with real-world applications, making it ideal for both beginners and experienced engineers.
Key takeaways:
- Design and manage robust ETL workflows using Databricks notebooks and workflows
- Leverage Delta Lake for reliable, scalable, and ACID-compliant storage
- Integrate MLflow for seamless machine learning lifecycle management
- Build real-time streaming pipelines using Structured Streaming
This book is a one-stop resource for mastering data engineering on Databricks with a focus on performance, scalability, and production-readiness.
👉 [Link to the book]
2️⃣ Data Engineering with AWS
Harnessing Modern Cloud Tools to Build End-to-End Data Pipelines
This book is perfect for data engineers looking to architect and implement end-to-end pipelines using AWS-native services. It takes a hands-on approach by walking readers through ingestion, transformation, storage, and governance strategies.
Key takeaways:
- Get familiar with essential AWS tools like Glue, Lambda, S3, Athena, Redshift, and EMR
- Build batch and streaming pipelines with real-world datasets
- Learn to design with data governance, security, and cost-efficiency in mind
- Understand how to use data lakes, warehouses, and lakehouses effectively
Whether you’re working on enterprise-scale data flows or preparing for AWS certification, this book is a practical, up-to-date guide.
👉 [Link to the book]
🔗 Further Reading & References
- Mastering Snowflake Platform – Learn to design high-performance queries and scalable pipelines on Snowflake
- Mastering Spark SQL with Databricks – Deep dive into Spark’s powerful query engine and optimization techniques for Databricks
Srinimf 