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The Friendly Guide to Apache Beam with Python
Stop Drowning in Data. Start Building with Apache Beam & Python.
Your Fast, Friendly, and Hands-On Guide to Mastering Real-World Data Pipelines.
The Friendly Guide to Apache Beam with Python cuts through the noise. Forget dense theory and academic jargon. This book gets you building practical, robust data pipelines from page one, using a unified model that handles both batch and streaming data with the same code.
Why Apache Beam is a Game-ChangerWrite Once, Run Create your pipeline logic once and execute it on your local machine for testing (DirectRunner), or scale it effortlessly to powerful engines like Apache Flink, Spark, and Google Cloud Dataflow without a rewrite.
Future-Proof Your Free yourself from being locked into a single technology. If a faster, better execution engine appears tomorrow, your Beam logic is ready to go.
Unified Batch & Use a single, elegant programming model to process everything from last year's sales records to a live feed of fraudulent transactions as they happen.
What You'll Master, Build a Rock-Solid Effortlessly master the core of Pipelines, PCollections (your supercharged data), and PTransforms (the workhorse operations).
Become a Data-Shaping Wield essential tools like Map, Filter, and FlatMap to reshape data, and use the powerful CombinePerKey for super-efficient aggregations.
Conquer Streaming Go beyond batch processing and learn to use Windowing (including Fixed, Sliding, and Session windows) to analyze user behavior and moving averages in real-time.
Build Smarter, Robust Enrich your data on the fly with Side Inputs, and cleanly separate good and bad records using Tagged Outputs and dead-letter files.
Test and Monitor Like a Write unit tests to validate your pipeline's logic and utilize built-in metrics, such as distributions and Gauges, to monitor its health in a production environment.
From your very first script to a complete, real-world ETL project, this guide provides clear explanations and hands-on code to make you a confident and effective data engineer.
Your Fast, Friendly, and Hands-On Guide to Mastering Real-World Data Pipelines.
The Friendly Guide to Apache Beam with Python cuts through the noise. Forget dense theory and academic jargon. This book gets you building practical, robust data pipelines from page one, using a unified model that handles both batch and streaming data with the same code.
Why Apache Beam is a Game-ChangerWrite Once, Run Create your pipeline logic once and execute it on your local machine for testing (DirectRunner), or scale it effortlessly to powerful engines like Apache Flink, Spark, and Google Cloud Dataflow without a rewrite.
Future-Proof Your Free yourself from being locked into a single technology. If a faster, better execution engine appears tomorrow, your Beam logic is ready to go.
Unified Batch & Use a single, elegant programming model to process everything from last year's sales records to a live feed of fraudulent transactions as they happen.
What You'll Master, Build a Rock-Solid Effortlessly master the core of Pipelines, PCollections (your supercharged data), and PTransforms (the workhorse operations).
Become a Data-Shaping Wield essential tools like Map, Filter, and FlatMap to reshape data, and use the powerful CombinePerKey for super-efficient aggregations.
Conquer Streaming Go beyond batch processing and learn to use Windowing (including Fixed, Sliding, and Session windows) to analyze user behavior and moving averages in real-time.
Build Smarter, Robust Enrich your data on the fly with Side Inputs, and cleanly separate good and bad records using Tagged Outputs and dead-letter files.
Test and Monitor Like a Write unit tests to validate your pipeline's logic and utilize built-in metrics, such as distributions and Gauges, to monitor its health in a production environment.
From your very first script to a complete, real-world ETL project, this guide provides clear explanations and hands-on code to make you a confident and effective data engineer.
36 pages, Kindle Edition
Published July 8, 2025
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