We’ll talk about Apache Beam in this guide and discuss its fundamental concepts. We will begin by showing the features and advantages of using Apache Beam, and then we will cover basic concepts and terminologies.
Ever since the concept of big data got introduced to the programming world, a lot of different technologies and frameworks have emerged. The processing of data can be categorized into two different paradigms. One is Batch Processing, and the other is Stream Processing.
Different technologies came into existence for different paradigms, solving various big data world problems, e.g., Apache Spark, Apache Flink, Apache Storm, etc.
As a developer or a business, it’s always challenging to maintain different tech stacks and technologies. Hence, Apache Beam to the rescue!
What is Apache Beam?
It is an open-source, centralized model for describing parallel-processing pipelines for both batch and streaming data. The programming model of the Apache Beam simplifies large-scale data processing dynamics.
The model offers helpful abstractions that insulate you from distributed processing information at low levels, such as managing individual staff, exchanging databases, and other activities. This low-level information is handled entirely by Dataflow.
Features of Apache Beam
The unique features are as follows:
- Unified – Use a single programming model for both batch and streaming use cases.
- Portable – Execute pipelines in multiple execution environments. Here, execution environments mean different runners. Ex. Spark Runner, Dataflow Runner, etc
- Extensible – Write custom SDKs, IO connectors, and transformation libraries.
Apache Beam SDKs and Runners
As of today, there are 3 Apache beam programming SDKs
Beam Runners translate the beam pipeline to the API-compatible backend processing of your choice. Beam currently supports runners that work with the following backends.
- Apache Spark
- Apache Flink
- Apache Samza
- Google Cloud Dataflow
- Hazelcast Jet
Direct Runner to run on the host machine, which is used for testing purposes.
Basic Concepts in Apache Beam
It has three main abstractions. They are
A pipeline is the first abstraction to be created. It holds the complete data processing job from start to finish, including reading data, manipulating data, and writing data to a sink. Every pipeline takes in options/parameters that indicate where and how to run.
A pcollection is an abstraction of distributed data. A pcollection can be bounded, i.e., finite data, or unbounded, i.e., infinite data. The initial pcollection is created by reading data from the source. From then on, pcollections are the source and sink of every step in the pipeline.
A transform is a data processing operation. A transform is applied on one or more pcollections. Complex transforms have other transforms nested within them. Every transformation has a generic apply method where the logic of the transformation sits in.
In this blog, we have understood the advantages of Apache beam and how it can help be used to transform complex data in pipelines. In our next blog, we will look at the practical implementation and will do some hands-on by creating the pipeline and transforming the data.
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