Dataflow Operations: Google Cloud Processing

This module provides an overview of the course structure and main topics to be covered. It serves as a foundation for understanding the course objectives and learning path.

This module explores the Jobs List page and filtering capabilities for monitoring Dataflow jobs. It covers the Job Graph, Job Info, and Job Metrics tabs for comprehensive job monitoring, as well as integration with Metrics Explorer for creating alerting policies.

This module focuses on utilizing the Log panel within the Job Graph and Job Metrics pages, along with understanding the centralized Error Reporting page for tracking and managing pipeline issues.

This module covers comprehensive troubleshooting and debugging techniques for Dataflow pipelines, including the four common failure modes: pipeline building failures, pipeline startup failures, execution failures, and performance issues.

This module examines key performance considerations for developing both batch and streaming pipelines in Dataflow, focusing on optimization techniques and best practices for efficient pipeline execution.

This module covers unit testing approaches for Dataflow pipelines and introduces frameworks and features for implementing effective CI/CD workflows to streamline pipeline development and deployment.

This module explores methods for building resilient systems that can handle corrupted data and data center outages, ensuring robust and reliable pipeline operations.

This module introduces Flex Templates as a solution for standardizing and reusing Dataflow pipeline code, addressing various operational challenges through template-based development.

This module provides a comprehensive review of all topics covered throughout the course, reinforcing key concepts and learning objectives.