Beam pipelines are portable between batch and streaming semantics but not every Runner is equally capable. The Apache Flink Runner supports Python, and it has good features that allow us to develop streaming pipelines effectively. We first discuss the portability layer of Apache Beam as it helps understand (1) how a pipeline developed by the Python SDK can be executed in the Flink Runner that only understands Java JAR and (2) how multiple SDKs can be used in a single pipeline. Then we move on to how to manage local Flink and Kafka clusters using bash scripts. Finally, we end up illustrating a simple streaming pipeline, which reads and writes website visit logs from and to Kafka topics.

In this series, we discuss local development of Apache Beam pipelines using Python. A basic Beam pipeline was introduced in Part 1, followed by demonstrating how to utilise Jupyter notebooks, Beam SQL and Beam DataFrames. In this post, we discuss Batch pipelines that aggregate website visit log by user and time. The pipelines are developed with and without Beam SQL. Additionally, each pipeline is implemented on a Jupyter notebook for demonstration.

Apache Beam and Apache Flink are open-source frameworks for parallel, distributed data processing at scale. Flink has DataStream and Table/SQL APIs and the former has more capacity to develop sophisticated data streaming applications. The DataStream API of PyFlink, Flink’s Python API, however, is not as complete as its Java counterpart, and it doesn’t provide enough capability to extend when there are missing features in Python. On the other hand, Apache Beam supports more possibility to extend and/or customise its features. In this series of posts, we discuss local development of Apache Beam pipelines using Python. In Part 1, a basic Beam pipeline is introduced, followed by demonstrating how to utilise Jupyter notebooks for interactive development. It also covers Beam SQL and Beam DataFrames examples on notebooks. In subsequent posts, we will discuss batch and streaming pipeline development and concludes with illustrating unit testing of existing pipelines.