Serverless Data Processing with Dataflow: Operations

Overview

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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

Introduction

Course Introduction

Monitoring

Job List
Job Info
Job Graph
Job Metrics
Metrics Explorer
Quiz
Additional Resources

Logging and Error Reporting

Logging
Error Reporting
Quiz
Additional Resources

Troubleshooting and Debug

Troubleshooting workflow
Types of troubles
Quiz
Serverless Data Processing with Dataflow - Monitoring, Logging and Error Reporting for Dataflow Jobs
Additional Resources

Performance

Pipeline Design
Data Shape
Source, Sinks & external systems
Shuffle and streaming engine
Quiz
Additional Resources

Testing and CI/CD

Testing and CI/CD Overview
Unit Testing
Integration Testing
Artifact Building
Deployment
Quiz
Serverless Data Processing with Dataflow - Testing with Apache Beam (Java)
Serverless Data Processing with Dataflow - Testing with Apache Beam (Python)
Serverless Data Processing with Dataflow - CI/CD with Dataflow
Additional Resources

Reliability

Introduction to Reliability
Monitoring
Geolocation
Disaster Recovery
High Availability
Quiz
Additional Resources

Flex Templates

Classic templates
Flex templates
Using flex templates
Google provided templates
Quiz
Serverless Data Processing with Dataflow - Custom Dataflow Flex Templates (Java)
Serverless Data Processing with Dataflow - Custom Dataflow Flex Templates (Python)
Additional Resources