3 day course 
Partner of the Year 
Private Course Credits
Select the pre-paid training investment that’s right for you and help your money stretch a little further with our course credits.
As a Google Cloud Partner, Jellyfish has been selected to deliver this three-day course, which will help you meet day-to-day data processing needs within your business.
Our expert practitioner will start with the foundations, showing you how Apache Beam and Dataflow work together to meet your data processing needs efficiently 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. Toward the end of the session, you’ll focus on operations, reviewing the most important lessons for operating a data application on Dataflow, including monitoring, troubleshooting, testing, and reliability.
Our Serverless Data Processing with Dataflow course is available as a private training session that can be delivered via Virtual Classroom or at a location of your choice in South Africa.
Course overview
Who should attend:
This course is suitable for data engineers, data analysts and data scientists aspiring to develop data engineering skills.
What you'll learn:
By the end of this course, you 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
Prerequisites
To get the most out of this course, you should have an understanding of building batch data pipelines and building resilient streaming analytics systems.
Course agenda
- Introduce the course objectives
- Demonstrate how Apache Beam and Dataflow work together to fulfill your organization's data processing needs
- 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
- Enable Shuffle and Streaming Engine, for batch and streaming pipelines respectively, for maximum performance
- Enable Flexible Resource Scheduling for more cost-efficient performances
- Select the right combination of IAM permissions for your Dataflow job
- Determine your capacity needs by inspecting the relevant quotas for your Dataflow jobs
- Select your zonal data processing strategy using Dataflow, depending on your data locality needs
- Implement best practices for a secure data processing environment
- Review main Apache Beam concepts (Pipeline, PCollections, PTransforms, Runner, reading / writing, Utility PTransforms, side inputs) bundles and DoFn Lifecycle
- Implement logic to handle your late data
- Review different types of triggers
- Review cores streaming concepts (unbounded PCollections, windows)
- 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
- Introduce schemas, which give developers a way to express structured data in their Beam pipeliness
- Use schemas to simplify your Beam code and improve the performance of your pipeline
- Identify use cases for state and timer API implementations
- Select the right type of state and timers for your pipeline
- Implement best practices for Dataflow pipelines
- Develop a Beam pipeline using SQL and DataFrames
- Prototype your pipeline in Python using Beam notebooks
- Launch a job to Dataflow from a notebook
- Navigate the Dataflow Job Details UI
- Interpret Job Metrics charts to diagnose pipeline regressions
- Set alerts on Dataflow jobs using Cloud Monitoring
- Use the Dataflow logs and diagnostics widgets to troubleshoot pipeline issues
- Use a structured approach to debug your Dataflow pipelines
- Examine common causes for pipeline failures
- Understand performance considerations for pipelines
- Consider how the shape of your data can affect pipeline performance
- Testing approaches for your Dataflow pipeline
- Review frameworks and features available to streamline your CI / CD workflow for Dataflow pipelines
- Implement reliability best practices for your Dataflow pipelines
- Using flex templates to standardize and reuse Dataflow pipeline code
- Summary of all modules