Serverless Data Processing with Dataflow

Introduction

• Introduce the course objectives.

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

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.

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.

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.

Security

• Select your zonal data processing strategy using Dataflow, depending on your data locality needs.

• Implement best practices for a secure data processing environment.

Beam Concepts Review

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

Windows, Watermarks, Triggers

• Implement logic to handle your late data.

• Review different types of triggers.

• Review core streaming concepts (unbounded PCollections, windows).

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.

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.

State and Timers

• Identify use cases for state and timer API implementations.

• Select the right type of state and timers for your pipeline.

Best Practices

• Implement best practices for Dataflow pipelines.

Dataflow SQL and DataFrames

• Develop a Beam pipeline using SQL and DataFrames.

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.

Monitoring

• Navigate the Dataflow Job Details UI.

• Interpret Job Metrics charts to diagnose pipeline regressions.

• Set alerts on Dataflow jobs using Cloud Monitoring.

Logging and Error Reporting

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

Troubleshooting and Debug

• Use a structured approach to debug your Dataflow pipelines.

• Examine common causes for pipeline failures.

Performance

• Understand performance considerations for pipelines.

• Consider how the shape of your data can affect pipeline performance.

Testing and CI/CD

• Testing approaches for your Dataflow pipeline.

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

Reliability

• Implement reliability best practices for your Dataflow pipelines.

Flex Templates

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

• Data Engineer

• Data Analysts and Data Scientists aspiring to develop Data Engineering skills

To get the most out of this course, participants should have some prior working experience with Dataflow.