Build, Test and Run Spark Applications at No Cost with StreamAnalytix Visual Spark Studio
Experience the Ease and Speed of Building Spark Application on Your Desktop. Free to download and use!
By Impetus. Sponsored Post.
Visual Spark Studio, a GUI based development and operations tool is an Integrated Development Environment (IDE) that helps developers build, test, run, deploy and manage Apache Spark applications quickly and easily.It enables developers to start building Spark pipelines within minutes using the intuitive drag and drop interface and a wide array of pre-built Spark operators.
Working with StreamAnalytix Visual Spark Studio is extremely easy
Visual Spark Studio is lightweight, less than 2GB on disk. Developers can download it onto their Windows, Mac, or a Linux desktop or a server node, and start to build, test, debug, deploy and manage Apache Spark applications end-to-end.
There is no need for coding, you can simply use a comprehensive set of pre-built Big Data tools and operators including an array of data sources, processors, analytical operators and emitters. Yet, Visual Spark Studio enables hand-written custom logic using custom-Java and custom-Scala operators.
“Productivity tools like Visual Spark Studio are key to bridging the current high talent barrier blocking wider Spark adoption,” said Mike Matchett, senior analyst, Taneja Group. “The Impetus StreamAnalytix team is aiming to help enterprises accelerate competitive analytics application development, and, at the same time, control the engineering and operational costs associated with complex machine learning workflows.”
Start building Spark pipelines within minutes on your desktop.
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StreamAnalytix™ is an open-source based, enterprise-grade, multi-engine platform for rapid and easy development of real-time streaming analytics applications. StreamAnalytix was recently cited as a strong performer in “The Forrester Wave™: Streaming Analytics, Q3 2017” report, and has been recognized in Aragon Research’s latest report “Hot Vendors in AI & Streaming Analytics, 2017.”
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