In the Serverless Data Processing with Dataflow: Operations course, you will delve into the operational model of Dataflow, mastering the tools and techniques for monitoring, troubleshooting, and optimizing pipeline performance. This comprehensive training will equip you with the skills to deploy Dataflow pipelines with reliability in mind, ensuring the stability and resilience of your data processing platform.
The course is designed to cover a wide array of essential topics, including:
By enrolling in this course, you will gain practical knowledge and hands-on experience through interactive labs and real-world examples, ensuring that you are well-prepared to manage and optimize Dataflow pipelines effectively.
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This course comprehensively covers essential topics such as monitoring, logging, troubleshooting, performance optimization, testing, reliability, and Flex Templates for Dataflow pipelines, providing a robust foundation for managing and optimizing data processing operations.
Throughout this module, you will receive an introduction to the course, including important information about hands-on labs and how to send feedback. You will also get started with Google Cloud Platform and Qwiklabs to familiarize yourself with the tools and resources available for the course.
This module covers monitoring aspects of Dataflow pipelines, including job list, information, graph, metrics, and the Metrics Explorer. You will also explore additional resources for comprehensive monitoring and analysis.
Delve into logging and error reporting for Dataflow jobs in this module, understanding how to effectively handle and report errors to ensure the smooth functioning of the data processing pipeline.
Learn about troubleshooting workflow, different types of issues, and gain hands-on experience with a lab focused on monitoring, logging, and error reporting for Dataflow jobs to apply your knowledge in a practical setting.
Optimize the performance of Dataflow pipelines through pipeline design, understanding data shape, source, sinks, external systems, shuffle, streaming engines, and other optimization techniques. Additional resources are also provided for further exploration.
Get an overview of testing and CI/CD, including unit testing, integration testing, artifact building, and deployment techniques. Engage in hands-on labs to apply testing with Apache Beam in Java and Python, as well as CI/CD with Dataflow.
Explore reliability principles, including monitoring, geolocation, disaster recovery, and high availability to ensure the resilience and stability of Dataflow pipelines. Additional resources are available for further understanding.
Gain insights into Flex Templates, including classic and custom Dataflow Flex Templates, and learn how to use Google provided templates effectively. Engage in labs focused on custom Dataflow Flex Templates in Java and Python to apply your knowledge hands-on.
Conclude the course with a comprehensive summary, assimilating the knowledge and skills acquired throughout the modules to solidify your understanding of Dataflow operations and best practices.
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