Apache Beam Tutorial: Data Processing, Windowing, and Streaming in Python
📌 Description:
In this tutorial, I demonstrate how to use Apache Beam for data processing, including batch and streaming pipelines. Learn to build powerful pipelines with Composite Transforms, handle File IO, implement Windowing for time-based grouping, and create custom ParDo transforms. This step-by-step guide is perfect for anyone looking to understand the basics of Apache Beam for scalable data processing.
🚀 What You'll Learn:
Setting up Apache Beam in Google Colab.
Using Composite Transforms for modular pipeline design.
Writing and reading data using File IO.
Implementing Windowing for time-based data grouping.
Creating and applying custom transformations with ParDo.
Simulating Streaming Pipelines for real-time data processing.
🎯 Key Concepts:
Apache Beam pipelines for batch and streaming.
Data transformations and modular pipeline design.
Real-world use cases like grouping data by time intervals and processing streams.
🔧 Tools & Libraries:
Apache Beam: For scalable, parallel data processing.
Google Colab: For running Apache Beam pipelines in a cloud-based environment.
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