Building and Running a Pipeline

Most organizations are already gathering and analyzing big data or plan to do so in the near future. One common way to process huge datasets is to use Apache Hadoop or Spark. Google even has a managed service for hosting Hadoop and Spark. It’s called Cloud Dataproc. So why do they also offer a competing service called Cloud Dataflow? Well, Google probably has more experience processing big data than any other organization on the planet and now they’re making their data processing software available to their customers. Not only that, but they’ve also open-sourced the software as Apache Beam.

Cloud Dataflow is a serverless data processing service that runs jobs written using the Apache Beam libraries. When you run a job on Cloud Dataflow, it spins up a cluster of virtual machines, distributes the tasks in your job to the VMs, and dynamically scales the cluster based on how the job is performing. It may even change the order of operations in your processing pipeline to optimize your job.

In this lesson, you will learn how to write data processing programs using Apache Beam and then run them using Cloud Dataflow. You will also learn how to run both batch and streaming jobs.

This is a hands-on lesson where you can follow along with the demos using your own Google Cloud account or a trial account.

Learning Objectives

• Write a data processing program in Java using Apache Beam
• Use different Beam transforms to map and aggregate data
• Use windows, timestamps, and triggers to process streaming data
• Deploy a Beam pipeline both locally and on Cloud Dataflow
• Output data from Cloud Dataflow to Google BigQuery

Resources

The Github repository for this lesson can be found at https://github.com/cloudacademy/beam.