What is Google Cloud Dataflow?

Google Cloud Dataflow is a tool that lets you build pipelines, oversee their execution, and transform and change data, all within the cloud. The tool is a natural evolution of MapReduce, Google’s erstwhile programming paradigm. At present, Google places its servers in Cloud Dataflow.

The tool in question facilitates companies that need solutions for large data analysis in order to free resources to focus on their own business. According to a blog post by Google, Cloud Dataflow enables you to get actionable insights from your data while reducing operational costs. It does so without needing deployment, maintenance or scaling of infrastructure. Since the project is very promising, Google has been working relentlessly towards the simplification of its developmental process and the monitoring of related operations.

Here’s a lowdown on the crucial features of Dataflow:

  • Multi-functionality: Google Cloud Dataflow can count ETL, process batches and stream real-time analytics, whereas most other database technologies are limited to just one speciality, like batch processing or super-fast analytics. Dataflow automatically optimizes, deploys and manages the code and resources required.
  • MapReduce’s next level: MapReduce, first developed by Google, is one of the core functions of Hadoop. Dataflow is the next level of MapReduce in the sense that it addresses the performance issues faced while building pipelines in the latter. MapReduce has, since quite some time now, been replaced by Dataflow at Google.
  • Big data compatible: MapReduce faltered majorly while dealing with multipetabyte datasets. Cloud Dataflow has no such issues.
  • Evolution from Flume and Milwheel: While Flume allows you to develop and run parallel pipelines for data processing, Millwheel lets you build low-latency data-processing applications.
  • Clean and clear coding model: It does not fail to impress when it comes to coding. The first SDK is for Java, the datasets are shown in parallel collections (PCollections), there is a rich library of parallel transforms (PTransforms) that includes the ParDo and GroupByKey function (similar to WHERE in SQL and Map and Reduce functions).

Cloud Dataflow is different from other similar tools, like Twitter’s Summingbird, because Google is only providing it as a service in the cloud, which can be accessed through the Internet by anyone. Through its services like Google App Engine and Google Compute Engine that let companies as well as independent developers to develop and run large software applications, Google is allowing its infrastructure in line to be shared with the world at large.

Have you worked with Cloud Dataflow? Tell us how your experience has been.

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