Serverless Data Processing with Dataflow: Foundations

Overview

This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. 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 >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service

Syllabus

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 are going to learn about four sections, Beam Portablity, Runner v2, Container Environments, 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.