Apache Beam

If you don’t like using multiple technologies to achieve lots of big data tasks then you need to consider Apache beam with a new distributed processing tool from Google that is currently developing at the ASF. Due to some difficulties of the big data development, there is a requirement for using various different technologies, frameworks, languages, APIs, and software development kits. An abundance of riches for big data developers has been offered by the open source movement and it has enhanced pressure on the developer to choose the perfect tool for the things she is wanting to accomplish.

This is quite difficult for those with a new development in big data application which could reduce or hinder the adoption of open source tools.

To remove some of the second-guessing the web giant is wanting to remove some painful tool-jumping along with Apache Beam which is placing a single programming and runtime model by not unifying development for interactive batch and streaming workflows but it also offers a single model for both on-premise and cloud development.

Depending on the technology used by Google it uses the Cloud Dataflow service which the company unveiled in 2014 for the current generation shared data processing challenges.

In the combination of the Dataflow Software Development Kit (SDK) the open source Apache Beam project along with the runner series extend out to run-time frameworks, Apache Flink, and Cloud Dataflow itself which can be freely tried by Google for charging you money in the usage of production.

A unified model is offered by Apache Beam for both designing and executing lots of data-oriented workflows within a data processing, data integration, and data ingestion as per the Apache Beam project page. Earlier the project was termed as Apache Dataflow before seeking the Apache beam moniker actually works on lots of Apache Software Foundation projects. The Beam runner for Flink is developed and maintained by the data Artisans and is joined by Google in the project.

Just consider you have a MapReduce job and now you need to combine these jobs with Spark which needs lots of works and cost. After this, the effort and cost you need to change to a new platform have to refactor your jobs again.

An abstraction layer is offered by data flow between the execution runtime and code. A unified programming model is permitted by the SKD for implementing your data processing logic with the help of Dataflow SDK that runs on various different backends.There is no need to refactor or change the code anymore.

In the Apache Beam SDK, there are four major constructs as per the Apache Beam proposal and they are:

  • Pipelines: There are few computations like input, output, and processing are the few data processing jobs actually made.
  • Pcollections: For representing the input there are some bounded datasets with intermediate and output data in pipelines.

For lots of batch processing or streaming goals, beams can be used similar to ETL, stream analysis and aggregate computation. For lots of batch processing goals or streaming is used by Beam like stream analysis and aggregate the computation.

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Reference site: datanami