In this first article, we’re exploring Apache Beam, from a simple pipeline to a more complicated one, using GCP Dataflow. Let’s learn what PTransform, PCollection, GroupByKey and Dataflow Flex Template mean
Without any doubt, processing data, creating features, moving data around, and doing all these operations within a safe environment, with stability and in a computationally efficient manner, is super relevant for all AI tasks nowadays. Back in the day, Google started to develop an open-source project to start both batching and streaming data processing operations, named Beam. Following, Apache Software Foundation has started to contribute to this project, bringing to scale Apache Beam.
The relevant key of Apache Beam is its flexibility, making it one of the best programming SDKs for building data processing pipelines. I would recognise 4 main concepts in Apache Beam, that make it an invaluable data tool:
- Unified model for batching/ streaming processing: Beam is a unified programming model, namely with the same Beam code you can decide whether to process data in batch or streaming mode, and the pipeline can be used as a template for other new processing units. Beam can automatically ingest a continuous stream of data or perform specific operations on a given batch of data.
- Parallel Processing: The efficient and scalable data processing core starts from the parallelization of the execution of the data processing pipelines, that distribute the workload across multiple “workers” — a worker can be intended as a node. The key concept for parallel execution is called “
ParDotransform”, which takes a function that processes individual elements and applies it concurrently across multiple workers. The great thing about this implementation is that you do not have to worry about how to split data or create batch-loaders. Apache Beam will do everything for you. - Data pipelines: Given the two aspects above, a data pipeline can be easily created in a few lines of code, from the data ingestion to the…