GoogleCloud: Serverless Data Processing with Dataflow: Foundations

• Demonstrate how Apache Beam and Cloud 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 & Streaming Engine for batch & 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

1. Introduction

• This module covers the course outline and does a quick refresh on the Apache Beam programming model and Google’s Dataflow managed service.

2. Beam Portability

• In this module we are going to learn about four sections, Beam Portability, Runner v2, Container Environments, and Cross-Language Transforms.

3. Separating Compute and Storage with Dataflow

• IIn this module we discuss how to separate compute and storage with Dataflow. This module contains four sections Dataflow, Dataflow Shuffle Service, Dataflow Streaming Engine, Flexible Resource Scheduling.

4. IAM, Quotas, and Permissions

• In this module, we talk about the different IAM roles, quotas, and permissions required to run Dataflow.

5. Security

• In this module, we will look at how to implement the right security model for your use case on Dataflow.

6. Summary

• In this course, we started with the refresher of what Apache Beam is, and its relationship with Dataflow.