IntroductionIntroduce the course objectives.Demonstrate how Apache Beam and Dataflow work together to fulfill your organization’s data processing needs.Beam PortabilitySummarize 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 DataflowEnable 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 PermissionsSelect the right combination of IAM permissions for your Dataflow job.Determine your capacity needs by inspecting the relevant quotas for your Dataflow jobs.SecuritySelect your zonal data processing strategy using Dataflow, depending on your data locality needs.Implement best practices for a secure data processing environment.Beam Concepts ReviewReview main Apache Beam concepts (Pipeline, PCollections, PTransforms, Runner, reading/writing, Utility PTransforms, side inputs), bundles and DoFn Lifecycle.Windows, Watermarks, TriggersImplement logic to handle your late data.Review different types of triggers.Review core streaming concepts (unbounded PCollections, windows).Sources and SinksWrite 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.SchemasIntroduce 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 TimersIdentify use cases for state and timer API implementations.Select the right type of state and timers for your pipeline.Best PracticesImplement best practices for Dataflow pipelines.Dataflow SQL and DataFramesDevelop a Beam pipeline using SQL and DataFrames.Beam NotebooksPrototype 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.MonitoringNavigate the Dataflow Job Details UI.Interpret Job Metrics charts to diagnose pipeline regressions.Set alerts on Dataflow jobs using Cloud Monitoring.Logging and Error ReportingUse the Dataflow logs and diagnostics widgets to troubleshoot pipeline issues.Troubleshooting and DebugUse a structured approach to debug your Dataflow pipelines.Examine common causes for pipeline failures.PerformanceUnderstand performance considerations for pipelines.Consider how the shape of your data can affect pipeline performance.Testing and CI/CDTesting approaches for your Dataflow pipeline.Review frameworks and features available to streamline your CI/CD workflow for Dataflow pipelines.ReliabilityImplement reliability best practices for your Dataflow pipelines.Flex TemplatesUsing flex templates to standardize and reuse Dataflow pipeline code.

Data EngineerData Analysts and Data Scientists aspiring to develop Data Engineering skillsTo get the most out of this course, participants should have some prior working experience with Dataflow.