Serverless Data Processing with Dataflow: Foundations (Coursera)

We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.

Prerequisites:

The Serverless Data Processing with Dataflow course series builds on the concepts covered in the Data Engineering specialization. We recommend the following prerequisite courses:

(i) Building batch data pipelines on Google Cloud : covers core Dataflow principles

(ii) Building Resilient Streaming Analytics Systems on Google Cloud : covers streaming basics concepts like windowing, triggers, and watermarks

Syllabus

WEEK 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.

Beam Portability

In this module, we cover 4 sections: Beam Portablity, Runner v2, Container Enviromnents, and Cross-Language Transforms.

Separating Compute and Storage with Dataflow

In 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.

WEEK 2

IAM, Quotas, and Permissions

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

Security

Summary

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