Why Apache Beam is the next big thing in big data processing
Introduction
Ever since Beam started in 2016 with Apache, it has been their top most project. Apache Beam is a unified programming model and a set of libraries for defining and executing data processing pipelines. It’s a programming model for writing big data processing pipelines which is portable and unified. Now what does it mean exactly:
First let’s understand the use cases for big data processing pipelines.
Batch processing: Batch processing is a data processing technique used in big data pipelines to analyze and process large volumes of data in batches or sets. In batch processing, data is collected over a period of time, and then the entire batch of data is processed together
Stream processing : Processing data as it is generated. It is a data processing technique to process data in real-time as it is generated, rather than in batches. In stream processing, data is processed continuously, as it flows through the pipeline.
Portable + Unified
Beam: Batch + Streaming
In older parallel processing frameworks, such as Hadoop, Spark or Flink, we had different APIs to handle batch and streaming data. For example, we have RDDs or dataframes to do batch processing in Apache Spark, whereas…
