By this post, I'll start a series of blog posts about creating new IO connectors in Apache Beam . Introduction to Beam IO Before getting into Beam IO internals, let's take a quick look on what actually Beam pipeline codebase is. In general, logically all code, that required to run a user pipeline, can be split into 4 layers - Runner , SDK , Transforms & IO , User Code . On the bottom level, there is a Runner code, which is responsible for all translations of user pipeline to make it possible to run on preferred data processing engine, like Apache Spark, Apache Flink, Google Dataflow, etc. On the second level, we have a SDK code. This part of code allows to write a Beam pipeline in favourite user programming language. For the moment, Beam supports the following SDKs: Java, Python and Go. Scala is supported through 3rd party SDK called Scio . Third level incorporates different Beam Transforms , like ParDo , GroupByKey , Combine , etc. Also, it includes