Top 9 Big Data Analytics Tools
1. Hadoop
Apache Hadoop is a framework for running applications on large cluster built of commodity hardware. The Hadoop framework transparently provides applications both reliability and data motion. Hadoop implements a computational paradigm named Map/Reduce, where the application is divided into many small fragments of work, each of which may be executed or re-executed on any node in the cluster. In addition, it provides a distributed file system (HDFS) that stores data on the compute nodes, providing very high aggregate bandwidth across the cluster. Both MapReduce and the Hadoop Distributed File System are designed so that node failures are automatically handled by the framework.
2. Cassandra
Apache Cassandra is an open source, distributed, massively scalable NoSQL database. It is designed to handle large volumes of structured, semi-structured and unstructured data across multiple data centers, and it supports the cloud.
3. Rapidminer
RapidMiner brings artificial intelligence to the enterprise through an open and extensible data science platform. Built for analytics teams, RapidMiner unifies the entire data science life cycle from data prep to machine learning to predictive model deployment. More than 500,000 analytics professionals use RapidMiner products to drive revenue, reduce costs, and avoid risks.
4. Wolfram Alpha
Building on Wolfram's three decades of technology development, the Wolfram|Alpha platform introduces knowledge-based computing—and fundamentally redefines knowledge presentation, acquisition, development, and management. For startups to global enterprises, Wolfram's product licensing and service organizations deliver innovative custom solutions for customers, informs organization, and energizes your data as never before. The introduction of Wolfram|Alpha defined a fundamentally new paradigm for getting knowledge and answers—not by searching the web, but by doing dynamic computations based on a vast collection of built-in data, algorithms and methods.
5. Storm
Apache Storm is a free and open source distributed real time computation system. Storm makes it easy to reliably process unbounded streams of data, doing for real time processing what Hadoop did for batch processing. Storm is simple, can be used with any programming language, and is a lot of fun to use! Storm has many use cases: real time analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate. Storm integrates with the queueing and database technologies you already use. A Storm topology consumes streams of data and processes those streams in arbitrarily complex ways, repartitioning the streams between each stage of the computation however needed. Read more in the tutorial.
https://storm.apache.org/about/simple-api.html
6. OpenRefine
OpenRefine, formerly called Google Refine and before that Freebase Gridworks, is a standalone open source desktop application for data cleanup and transformation to other formats, the activity known as data wrangling. It is similar to spreadsheet applications; however, it behaves more like a database.
7. Plotly
Plotly, also known by its URL, Plot.ly, is a technical computing company headquartered in Montreal, Quebec, that develops online data analytics and visualization tools. Plotly provides online graphing, analytics, and statistics tools for individuals and collaboration, as well as scientific graphing libraries for Python, R, MATLAB, Perl, Julia, Arduino, and REST.
8. Bokeh
Bokeh is an interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.
https://bokeh.pydata.org/en/latest/docs/user_guide/quickstart.html
9. Neo4j
Neo4j is an open-source, NoSQL, native graph database that provides an ACID-compliant transactional backend for your applications. Initial development began in 2003, but it has been publicly available since 2007. The source code, written in Java and Scala, is available for free on GitHub or as a user-friendly desktop application download.
