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Google Dataflow

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May 1, 2024 3 minute read
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  • A unified programming model for both streaming and batch data processing
  • Automatic scaling of resources based on the load
  • Fault tolerance and automatic retries
  • A variety of built-in connectors to popular data sources and sinks
  • Real-time analytics
  • Fraud detection
  • Recommendation engines
  • Data warehousing

Path to Google Dataflow

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We've curated two courses to help you on your path to Google Dataflow. Use these to develop your skills, build background knowledge, and put what you learn to practice.
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Reading list

We've selected two 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 Google Dataflow.
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Data Algorithms with Spark
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Provides a step-by-step guide to building and deploying data pipelines using Apache Beam and Google Dataflow. It covers various data sources and sinks.
Data Algorithms with Spark: Recipes and Design...
Paperback
$$$
Data Algorithms: Recipes for Scaling Up with Hadoop...
Paperback
$$
Data Algorithms with Spark: Recipes and Design...
Kindle Edition
$$$
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Designing Data-Intensive Applications
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While this book doesn't focus specifically on Apache Beam or Google Dataflow, it provides valuable insights into the design and architecture of data-intensive systems, which is relevant to anyone working with these technologies.
Designing Data-Intensive Applications: The Big...
Paperback
$$
Designing Data-Intensive Applications: The Big...
Kindle Edition
$$
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