Serverless Data Processing with Dataflow

Name: Serverless Data Processing with Dataflow
Price: 1000.00 KRW

This course is intended for big data practitioners who want to further their understanding of Dataflow in order to advance their data processing applications.

3 day course

Supporting material

Google Cloud Partner of the Year

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 Specialisation Partner of the Year. This title recognises 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 organisation'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 organisation's data processing needs

Module 2: Beam Portability

Summarise the benefits of the Beam Portability Framework
Customise 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 behaviour 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 standardise and reuse Dataflow pipeline code

Module 21: Summary

Summary of all modules

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