The Apache Software Foundation
The Apache Software Foundation (ASF) exists to provide software for the public good. We believe in the power of community over code, known as The Apache Way. Thousands of people around the world contribute to ASF open source projects every day.
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product
Web Application / Services Testing Tool
JMeter™
The Apache Software Foundation
Apache JMeter may be used to test performance both on static and dynamic resources (Webservices (SOAP/REST), Web dynamic languages - PHP, Java, ASP.NET, Files, etc. -, Java Objects, Data Bases and Queries, FTP Servers and more). It can be used to simulate a heavy load on a server, group of servers, network or object to test its strength or to analyze overall performance under different load types. You can use it to make a graphical analysis of performance or to test your server/script/object behavior under heavy concurrent load.
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High Performance Big Data Analytics
Apache Kudu
The Apache Software Foundation
Kudu is a columnar storage manager developed for the Hadoop platform. Kudu shares the common technical properties of Hadoop ecosystem applications: it runs on commodity hardware, is horizontally scalable, and supports highly available operation. Fast processing of OLAP workloads. Integration with MapReduce, Spark and other Hadoop ecosystem components.
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Open-source Interactive Data Analytics
Apache Zeppelin
The Apache Software Foundation
A web-based notebook that enables interactive data analytics.You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala and more.
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Open Source Machine Learning server
PredictionIO
The Apache Software Foundation
Apache PredictionIO is an open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learning task. It lets you: quickly build and deploy an engine as a web service on production with customizable templates; respond to dynamic queries in real-time once deployed as a web service; evaluate and tune multiple engine variants systematically;unify data from multiple platforms in batch or in real-time for comprehensive predictive analytics; speed up machine learning modeling with systematic processes and pre-built evaluation measures; etc.
