Serverless Data Processing with Dataflow: Part 1

Embark on an exciting journey into the world of serverless data processing with our comprehensive course, "Serverless Data Processing with Dataflow: Part 1." This intermediate-level course is the first installment of a three-part series designed to equip you with cutting-edge skills in Apache Beam and Google Cloud Dataflow. Dive deep into the powerful combination of these technologies and learn how to leverage them for efficient, scalable, and cost-effective data processing solutions.

What students will learn from the course:

  • Master the integration of Apache Beam and Cloud Dataflow for organizational data processing needs
  • Understand and implement the Beam Portability Framework for enhanced flexibility
  • Optimize performance using Shuffle & Streaming Engine for batch and streaming pipelines
  • Utilize Flexible Resource Scheduling for cost-efficient performance
  • Configure appropriate IAM permissions for Dataflow jobs
  • Implement best practices for secure data processing environments

Pre-requisite or skills necessary to complete the course:

While there are no specific prerequisites listed, a basic understanding of data processing concepts and cloud computing would be beneficial. Familiarity with programming concepts is also recommended.

What the course will cover:

  • Apache Beam and Dataflow fundamentals
  • Beam Portability Framework and its benefits
  • Runner v2 and Container Environments
  • Cross-Language Transforms
  • Dataflow Shuffle Service and Streaming Engine
  • Flexible Resource Scheduling
  • IAM roles, quotas, and permissions for Dataflow
  • Security models and best practices for Dataflow

Who this course is for:

This course is ideal for data engineers, cloud professionals, and software developers looking to expand their skills in serverless data processing. It's particularly suited for those interested in working with Google Cloud technologies and seeking to optimize their data processing workflows.

How learners can use these skills in the real world:

The skills acquired in this course are directly applicable to real-world scenarios in data engineering and cloud computing. Learners will be able to:

  • Design and implement efficient data processing pipelines
  • Optimize resource usage and reduce costs in cloud environments
  • Enhance data processing flexibility across different programming languages and execution backends
  • Implement robust security measures for sensitive data processing tasks
  • Improve overall performance of data processing workflows in enterprise environments

Syllabus:

1. Introduction

  • Course outline
  • Apache Beam programming model refresh
  • Google's Dataflow managed service overview

2. Beam Portability

  • Beam Portability concept
  • Runner v2
  • Container Environments
  • Cross-Language Transforms

3. Separating Compute and Storage with Dataflow

  • Dataflow overview
  • Dataflow Shuffle Service
  • Dataflow Streaming Engine
  • Flexible Resource Scheduling

4. IAM, Quotas, and Permissions

  • IAM roles for Dataflow
  • Quotas management
  • Required permissions for Dataflow operations

5. Security

  • Implementing security models for Dataflow use cases
  • Best practices for secure data processing

6. Summary

  • Course recap
  • Key takeaways

By enrolling in this course, you'll gain invaluable insights into serverless data processing, positioning yourself at the forefront of modern data engineering practices. Don't miss this opportunity to enhance your skills and advance your career in the rapidly evolving field of cloud data processing!