Data Engineering patterns
on the cloud

How to solve common data engineering problems with cloud services?
book cover
Release 🔥

What will you get from this book?

Cloud services discovery

Each pattern starts with a contextual explanation of the solved problem. It helps understand the use case scenario.

Tricky points

A pattern might require some extra care in implementation. The "Look at" section warns you about the tricky points.

AWS, Azure, GCP implementations

"Cloud details" part shows cloud providers implementation of the pattern. You can also go further by exploring the referenced links.

Implementation in the picture

New concepts can sometimes be difficult to understand. The "Schema design" image is there to facilitate the understanding.

91 data engineering patterns

The book covers data processing, data storage, data security, data warehouse, data management, data orchestration and data transfer categories.

Lifetime updates

The new release contains 91 patterns. There will be more and you'll get the new ebook version at every update.

What's Inside?

  • 91 data engineering patterns
  • PDF and EPUB versions.

Who This Book Is For

Cloud Data Engineer

If you're already working with one cloud provider, the book will help you extend and apply your knowledge to other providers.

Junior Data Engineer

If you have just started your data engineering adventure, you can use the book to structure your knowledge and discover new concepts.

Hadoop Data Engineer

If you've only worked with on-premise data systems, the book will help you adapt your skills to the cloud world.

Data team member

If you are another technical member of a data team , the book will help you better understand the data engineering world.

Backend Software Engineer

If you have never been a data engineer, you can get the book and learn data patterns for the backend services, such as object store, streaming broker, or NoSQL databases.

Pattern page

Check what's inside.

About The Author

Bartosz Konieczny is a data engineer by day and a blogger by night. In the last years he has specialized in multi-cloud data engineering by implementing Big Data pipelines on top of AWS, Azure and GCP for various industries (broadcasting, online video sharing platform, oil, worldwide dating app, cars real-time signals processing).

By night he shares his knowledge on cloud data services and Apache Spark on, Become a Better Data Engineer, and data conferences like Spark+AI Summit or Data+AI Summit. He was also nominated to the first group of Databricks Beacons.

YES, I want my copy!

Still not sure?

Check the first chapter for free 👇