Serverless Data Processing with Dataflow (SDPF) – Perfil

Esquema Detallado del Curso

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.