1. Hadoop
It would be impossible to talk about open source data analytics without mentioning Hadoop. This Apache Foundation project has become nearly synonymous with big data, and it enables large-scale distributed processing of extremely large data sets. A survey conducted by TDWI and SAS found that nearly 60 percent of enterprises expected to have Hadoop clusters in production by the end of 2016.
However, it should be noted that Hadoop on its own doesn't enable data analytics. It's usually part of a larger solution for gathering insights from big data.
2. Spark
Also an Apache project, Spark promises fast big data processing. In fact, it claims to "run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk." As a result of this fast performance, it is often used to analyze streaming data or in applications that require interactive analysis capabilities. Companies frequently use it alongside Hadoop or Mesos although it can also run on its own. It has recently experienced a dramatic rise in popularity, and a 2016 survey conducted by Syncsort found that nearly 70 percent of enterprise big data staffers surveyed were interested in Spark.
3. Talend
Unlike the first two projects in this slideshow, Talend is managed by a for-profit company rather than a foundation. As a result, paid support is available. Talend offers a mix of free and paid products. Its free, open source solution is called Talend Open Studio, and it has been downloaded more than 2 million times.
Market research firm Gartner recently named Talend a "Leader" in data integration. The company boasts that it can help enterprises analyze their big data five times faster and at one-fifth the cost compared to competing solutions.
4. Jaspersoft
Like Talend, Jaspersoft comes in multiple editions both free and paid. Its Community edition is free and open source while the Reporting, AWS, Professional and Enterprise editions require a fee but come with support included.
Jaspersoft is an open source business intelligence tool that aims to allow business users to self-serve their own needs. The company claims that its technology powers more than 130,000 apps with embedded BI capabilities.
5. Pentaho
Pentaho describes itself as a "comprehensive data integration and business analytics platform." The company primarily promotes the commercial versions of its software, which are based on the open source Community version. Companies can use it alongside tools like Hadoop and Spark to enable reporting and visualizations for their big data. This software boasts a long list of well-known customers that includes BT, Caterpillar, Nasdaq, The U.S. Dept. of Homeland Security, NOAA, The New York Times, EMC and many others.
6. RapidMiner
RapidMiner claims to be the "#1 open source data science platform," and Gartner named it a leader in its Magic Quadrant report for advanced analytics. It enables self-service predictive analytics and promises lightning-fast performance. Its users include BMW, Lufthansa, Domino's Pizza, Sony, Ford, Salesforce, Amnesty International and GE.The complete RadiMiner Platform includes three separate pieces: RapidMiner Studio, RapidMiner Server and RapidMiner Radoop. All three are available under open source or commercial licenses, and commercial prices depend on the number of users.
7. Storm
Used by companies like Yahoo, Twitter, Spotify, Yahoo, Yelp, Flipboard and Groupon, Apache Storm is a real-time big data processing engine. Its website explains, "Storm makes it easy to reliably process unbounded streams of data, doing for real-time processing what Hadoop did for batch processing." Customers can use it with any database and any programming language. It's scalable, fault-tolerant and easy to deploy. Users should note however, that Storm has not yet reached the 1.0 release level.
8. H2O
Used by more than 60,000 data scientists at more than 7,000 organizations, H2O claims to be "the world's leading open source machine learning platform." Thanks to its in-memory technology, it offers extremely fast performance. It also integrates with many other open source data analytics tools like Hadoop and Spark, and it supports all of the most popular databases. Paid support is available.
In addition to the standard version of H2O, the company also offers Sparkling Water, a version that incorporates Spark, and Steam, and end-to-end artificial intelligence application engine.
9. Lumify
Created by a company called Altamira Technologies, Lumify describes itself as an "open source big data analysis and visualization platform." It makes it easy to create 2D or 3D graphs that show the relationship between entities or to overlay data on maps. For those who are interested in learning more about how it works, the website offers several videos that show Lumify in action, and it also has a demo site that allows users to upload their own data and try out the software.
10. Drill
Apache Drill allows users to use SQL queries for non-relational data storage systems. It supports a range of NoSQL and cloud-based data storage systems, including HBase, MongoDB, MapR-DB, HDFS, MapR-FS, Amazon S3, Azure Blob Storage, Google Cloud Storage and Swift. It also allows users to search through multiple datasets stored with different technologies using a single query. In addition, it supports many popular BI tools.
11. MongoDB
One of the best-known NoSQL databases, MongoDB is an open-source non-relational data storage solution. Its customers include MetLife, the city of Chicago, Expedia, Google, The Weather Channel, BuzzFeed and Facebook. In addition to the free open source version, the company also offers a paid Enterprise version and MongoDB Atlas, a cloud-hosted version. Forrester has named MongoDB a "Leader" for big data NoSQL.
12. SpagoBI
SpagoBI is an open source business intelligence and big data analytics platform. The software is completely free, but paid user support, maintenance, consulting and training are available for purchase. It includes tools for reporting, multidimensional analysis (OLAP), charts, location intelligence, data mining, ETL and more. It also integrates with popular in-memory processing engines and enables real-time processing.