How To Create Dataflow Job with Scio

A group of brilliant engineers in Google led by <a href="https://www.linkedin.com/in/paulnordstrom/" rel="noopener ugc nofollow" target="_blank">Paul Nordstrom</a> wants to create a system that does the streaming data process that MapReduce did for batch data processing. They wanted to provide a robust abstraction and scale to a massive size. Building MillWheel was no easy feat. Testing and ensuring correctness in the streaming system was especially challenging because it couldn’t be rerun like a batch pipeline to produce the same output. As if that wasn’t enough, the Lambda architecture complicated matters further, making it difficult to aggregate and reconcile streaming and batch results. Out of such adversity, Google Dataflow was born- a solution combining the best of both worlds into one unified system serving batch and streaming pipelines. Creating and designing pipelines is a different thought process and framework from writing custom applications. For the past few months, I have spent numerous days and weeks learning the fundamentals and concepts of Apache Beam and Dataflow job to build a dataflow pipeline for my projects. There aren’t as many articles that briefly introduce Dataflow, Apache Beam, and Scio that you can read while commuting by train or bus to work. Thus, I hope this article helps all beginners like me to wrap their heads around these concepts. <a href="https://levelup.gitconnected.com/how-to-create-dataflow-job-with-scio-9e294f1a4b32">Visit Now</a>