Serverless Data Processing with Dataflow

After completing the Serverless Data Processing with Dataflow course, students will be able to:

• Demonstrate how Apache Beam and Dataflow work together to fulfill your organization’s data processing needs.
• Summarize the benefits of the Beam Portability Framework and enable it for your Dataflow pipelines.
• Enable Shuffle and Streaming Engine, for batch and streaming pipelines respectively, for maximum performance.
• Enable Flexible Resource Scheduling for more cost-efficient performance.
• Select the right combination of IAM permissions for your Dataflow job.
• Implement best practices for a secure data processing environment.
• Select and tune the I/O of your choice for your Dataflow pipeline.
• Use schemas to simplify your Beam code and improve the performance of your pipeline.
• Develop a Beam pipeline using SQL and DataFrames.
• Perform monitoring, troubleshooting, testing and CI/CD on Dataflow pipelines.

• Data engineer.
• Data analysts and data scientists aspiring to develop data engineering skills

To get the most out of this course, participants should have completed the following courses:
• “Building Batch Data Pipelines”
• “Building Resilient Streaming Analytics Systems”

Module 1: Introduction

• Introduce the course objectives.
• Demonstrate how Apache Beam and Dataflow work together to fulfill your organization’s data processing needs.

Module 2: Beam Portability

• Summarize the benefits of the Beam Portability Framework.
• Customize the data processing environment of your pipeline using custom containers.
• Review use cases for cross-language transformations.
• Enable the Portability framework for your Dataflow pipelines.

Module 3: Separating Compute and Storage with Dataflow

• Enable Shuffle and Streaming Engine, for batch and streaming pipelines respectively, for maximum performance.
• Enable Flexible Resource Scheduling for more cost-efficient performance.

Module 4: IAM, Quotas, and Permissions

• Select the right combination of IAM permissions for your Dataflow job.
• Determine your capacity needs by inspecting the relevant quotas for your Dataflow jobs.

Module 5: Security

• Select your zonal data processing strategy using Dataflow, depending on your data locality needs.
• Implement best practices for a secure data processing environment.

Module 6: Beam Concepts Review

• Review main Apache Beam concepts (Pipeline, PCollections, PTransforms, Runner, reading/writing, Utility PTransforms, side inputs), bundles and DoFn Lifecycle.

Module 7: Windows, Watermarks, Triggers

• Implement logic to handle your late data.
• Review different types of triggers.
• Review core streaming concepts (unbounded PCollections, windows).

Module 8: Sources and Sinks

• Write the I/O of your choice for your Dataflow pipeline.
• Tune your source/sink transformation for maximum performance.
• Create custom sources and sinks using SDF.

Module 9: Schemas

• Introduce schemas, which give developers a way to express structured data in their Beam pipelines.
• Use schemas to simplify your Beam code and improve the performance of your pipeline.

Module 10: State and Timers

• Identify use cases for state and timer API implementations.
• Select the right type of state and timers for your pipeline.

Module 11: Best Practices

• Implement best practices for Dataflow pipelines.

Module 12: Dataflow SQL and DataFrames

• Develop a Beam pipeline using SQL and DataFrames.

Module 13: Beam Notebooks

• Prototype your pipeline in Python using Beam notebooks.
• Use Beam magics to control the behavior of source recording in your notebook.
• Launch a job to Dataflow from a notebook.

Module 14: Monitoring

• Navigate the Dataflow Job Details UI.
• Interpret Job Metrics charts to diagnose pipeline regressions.
• Set alerts on Dataflow jobs using Cloud Monitoring.

Module 15: Logging and Error Reporting

• Use the Dataflow logs and diagnostics widgets to troubleshoot pipeline issues.

Module 16: Troubleshooting and Debug

• Use a structured approach to debug your Dataflow pipelines.
• Examine common causes for pipeline failures.

Module 17: Performance

• Understand performance considerations for pipelines.
• Consider how the shape of your data can affect pipeline performance.

Module 18: Testing and CI/CD

• Testing approaches for your Dataflow pipeline.
• Review frameworks and features available to streamline your CI/CD workflow for Dataflow pipelines.

Module 19: Reliability

• Implement reliability best practices for your Dataflow pipelines.

Module 20: Flex Templates

• Using flex templates to standardize and reuse Dataflow pipeline code.

Module 21: Summary

• Summary