Introduction to Apache Beam

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What is Apache Beam?

Apache Beam is a unified programming model for batch and streaming data processing jobs. It provides a software development kit to define and construct data processing pipelines as well as runners to execute them.

Apache Beam is designed to give a portable programming layer. The Beam Pipeline Runners translate the data processing pipeline into the API compatible with the back-end of the user’s choice. presently, these distributed processing back-ends are supported which include Apache Flink, Apache Spark, and Google Cloud Dataflow.

You can also use Beam for Extract, Transform, and Load (ETL) tasks and data integration. These tasks are useful for moving data between different storage media and data sources, transforming data into a more desirable format, or loading data onto a new system.

Why Apache Beam is important?

Apache Beam came into existence is that it can be used in parallel data processing tasks which can be divided into several smaller chunks of data which are processed in parallel and independently. The advantage of using beam SDK is that it can transform a dataset of any size whether the input data is finite i.e., came from a batch data source or an infinite data set from a streaming source of data. Other benefits of using Beam SDK are that it uses the same class to the representation of both the bounded and unbounded data. Currently, Beam supports the following language-specific SDKs –

Java
Python
Go

Fundamental Concepts

Pipelines-

A pipeline enclose the entire data processing tasks from start to end. Stages involved in this are reading the input data, transforming that data, and after that, writing the output. The input source and output sink can be the same or of different types, allowing you to convert data from one format to another. Apache Beam programs start by constructing a Pipeline object, and then using that object as the basis for creating the pipeline’s datasets. Each pipeline represents a single, repeatable job.

PCollection

A PCollection represents a distributed, multi-element dataset that acts as the pipeline’s data. Apache Beam transforms use PCollection objects as inputs and outputs for each step in your pipeline. A PCollection can hold a dataset of a fixed size or an unbounded dataset from a continuously updating data source.

Transforms

A transform represents a processing operation that transforms data. A transform takes one or more PCollections as input, performs an operation that you specify on each element in that collection, and produces one or more PCollections as output. transform can perform nearly any kind of processing operation, including performing mathematical computations on data, converting data from one format to another, grouping data together, reading and writing data, filtering data to output only the elements you want, or combining data elements into single values.

ParDo

ParDo is the core parallel processing operation in the Apache Beam SDKs, invoking a user-specified function on each of the elements of the input PCollection. ParDo collects the zero or more output elements into an output PCollection. The ParDo transform processes elements independently and possibly in parallel.

Pipeline I/O

Apache Beam I/O connectors let you read data into your pipeline and write output data from your pipeline. An I/O connector consists of a source and a sink. All Apache Beam sources and sinks are transforms that let your pipeline work with data from several different data storage formats. You can also write a custom I/O connector.

Aggregation

Aggregation is the process of computing some value from multiple input elements. The primary computational pattern for aggregation in Apache Beam is to group all elements with a common key and window. Then, it combines each group of elements using an associative and commutative operation.

User-defined functions (UDFs)

Some operations within Apache Beam allow executing user-defined code as a way of configuring the transform. For ParDo, user-defined code specifies the operation to apply to every element, and for Combine, it specifies how values should be combined. A pipeline might contain UDFs written in a different language than the language of your runner. A pipeline might also contain UDFs written in multiple languages.

Runner

Runners are the software that accepts a pipeline and executes it. Most runners are translators or adapters to massively parallel big-data processing systems. Other runners exist for local testing and debugging.

Source

A transform that reads from an external storage system. A pipeline typically reads input data from a source. The source has a type, which may be different from the sink type, so you can change the format of data as it moves through the pipeline.