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Dataflow

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May 1, 2024 Updated June 2, 2025 23 minute read

Understanding Dataflow: A Comprehensive Guide

Dataflow, at its core, represents a paradigm for thinking about and executing computation. Instead of focusing on a sequence of commands that manipulate a shared state (as in traditional imperative programming), dataflow emphasizes the movement of data and the transformations applied to it as it travels through a system. Imagine a series of interconnected processing stations on an assembly line; data enters, is processed at each station, and then moves to the next. This model is particularly well-suited for handling large volumes of information and performing operations in parallel, making it a cornerstone of many modern computing systems.

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Reading list

We've selected three books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Dataflow.
Provides a collection of recipes for solving common problems when working with Google Dataflow. It covers a wide range of topics, from basic tasks such as reading and writing data to more advanced topics such as streaming analytics and machine learning.
Provides a comprehensive overview of how to use Python for building data pipelines. While it does not focus specifically on Google Dataflow, it valuable resource for anyone who wants to understand the basics of data pipeline development.
Provides a comprehensive overview of how to use Hadoop and Spark for big data analytics. While it does not focus specifically on Google Dataflow, it valuable resource for anyone who wants to understand the broader context in which Dataflow operates.
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