Serverless Data Processing with Dataflow: Operations

Overview

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.

Syllabus

1. Introduction

This module covers the course outline

2. Monitoring

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.

3. Logging and Error Reporting

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.

4. Troubleshooting and Debug

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.

5. Performance

In this module, we will discuss performance considerations we should be aware of while developing batch and streaming pipelines in Dataflow.

6. Testing and CI/CD

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.

7. Reliability

In this module we will discuss methods for building systems that are resilient to corrupted data and data center outages.

8. Flex Templates

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.

9. Summary