Open Source Big Data Tools
According to the Gartner IT Glossary- Big data consist of high variety, high volume and high velocity information. Assets demand cost effective, innovative forms of information processing for enhanced insight and decision making.
The spark is an open source fast and general engine for large-scale data processing. It supported across platforms. It enables you to write applications quickly in Java, Scala, Python, R. It provides the support for group chat, Computer telephony integration(CTI),and strong security. It can be run on on Hadoop, Mesos, standalone, or in the cloud. It provides excellent end user experience. It provides the features of tabbed conversations, group chat, in-line spell checking etc
Apache Storm is an open source distributed computation framework. It is written in predominantly in the Clojure programming language. It can be used with any programming language. It is very simple and easy to reliably process unbounded streams of data, doing in real time processing what Hadoop did for batch processing. It is supported across platforms and released under Apache License 2.0.
H2O is another excellent open source tool for big-data analysis. It lunched in Silicon Valley in 2011. It is a flexible and speedy solution. It enables users to easily apply math and predictive analytic to solve today’s most challenging business problems. It allows users to fit hundreds or thousands of potential models as part of discovering patterns in data. It supported Linux, Mac OS, and Microsoft Windows operating system. It is written in Java, Python and R language.
Apex consist of enterprise grade native YARN big data-in-motion platform. It unifies stream processing as well as batch processing. It processes big data in highly scalable, highly perform-ant, stateful, secure, distributed, fault tolerant, and an easily operable way. It provides the features of event processing guarantees, performance & scalability, a Hadoop-native YARN & HDFS implementation etc.
The Druid is an open source column-oriented distributed data store. It is designed for business intelligence (OLAP) queries on event data. It is written in Java language and supported across platforms. It provides real time data ingestion, low latency, fast data aggregation, and flexible data exploration. It is used to power user-facing analytic applications. It provides the features of power analytic applications, cost effective and scalable.