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Serverless Data Processing with Dataflow

This course is intended for big data practitioners who want to further their understanding of Dataflow in order to advance their data processing applications.
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3 day course
Supporting material
Google Cloud Partner of the Year
Private
Private
A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

Beginning with foundations, this course explains how Apache Beam and Dataflow work together to meet your data processing needs without the risk of vendor lock-in.

The section on developing pipelines will show you how you convert your business logic into data processing applications that can run on Dataflow. The training session culminates with a focus on operations, which reviews the most important lessons for operating a data application on Dataflow, including monitoring, troubleshooting, testing, and reliability.

Jellyfish has recently been named a Google Cloud Specialization Partner of the Year. This title recognizes our commitment to provide world-leading Cloud-based Training solutions that help our clients succeed.

Course overview
Who should attend:

Data engineers, Data Analysts and Data Scientists aspiring to develop data engineering skills.

Walk away with the ability to:
  • Demonstrate how Apache Beam and Dataflow work together to fulfill your organization's data processing needs
  • 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
Prerequisites
To get the most out of this course, participants should have an understanding of building batch data pipelines and building resilient streaming analytics systems.
Course agenda
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 performances
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 cores 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 of all modules
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