Which one of the following is not a consideration for designing performant pipelines in Dataflow?
本练习是课程的一部分
通过我们的互动练习之一，将理论转化为实践
This module covers the course outline
In this module, we learn how to use the Jobs List page to filter for jobs that we want to monitor or investigate. We look at how the Job Graph, Job Info, and Job Metrics tabs collectively provide a comprehensive summary of your Dataflow job. Lastly, we learn how we can use Dataflow’s integration with Metrics Explorer to create alerting policies for Dataflow metrics.
In this module, we learn how to use the Log panel at the bottom of both the Job Graph and Job Metrics pages, and learn about the centralized Error Reporting page.
In this module, we learn how to troubleshoot and debug Dataflow pipelines. We will also review the four common modes of failure for Dataflow: failure to build the pipeline, failure to start the pipeline on Dataflow, failure during pipeline execution, and performance issues.
In this module, we will discuss performance considerations we should be aware of while developing batch and streaming pipelines in Dataflow.
当前练习
This module will discuss unit testing your Dataflow pipelines. We also introduce frameworks and features available to streamline your CI/CD workflow for Dataflow pipelines.
In this module we will discuss methods for building systems that are resilient to corrupted data and data center outages.
This module covers Flex Templates, a feature that helps data engineering teams standardize and reuse Dataflow pipeline code. Many operational challenges can be solved with Flex Templates.
This module reviews the topics covered in the course
