In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.
Pipeline Design
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Reviews
3.6 (17 ratings)
- 5 stars47.05%
- 4 stars11.76%
- 3 stars11.76%
- 2 stars11.76%
- 1 star17.64%
RR
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Labs are keeping up-to-date, but are lacking overall theoretical summary to teach symmatically how each code could work. Still a very typical problem of courses offered by Google Cloud.
RT
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Good intermediate course covering the big picture about how to develop data platforms using GCP and Dataflow.
From the lesson
Performance
In this module, we will discuss performance considerations we should be aware of while developing batch and streaming pipelines in Dataflow.
Taught By
Google Cloud Training