The scalable, open source
big data analytics platform
for networks and services.

A modular scientific software framework. It provides all the functionalities needed to deal with big data processing, statistical analysis, visualisation and storage. It is mainly written in C++ but integrated with other languages such as Python and R.

A user-friendly, map-based tool to combine and explore real-time or historical data.

WhereHows is a data discovery and lineage tool built at LinkedIn. It integrates with all the major data processing systems and collects both catalog and operational metadata from them.

high performance, high scalability and high reliability database for big data.
GridDB has a KVS (Key-Value Store)-type data model that is suitable for sensor data stored in a timeseries. It is a database that can be easily scaled-out according to the number of sensors.

Apache HBase™ is the Hadoop database, a distributed, scalable, big data store.
Use Apache HBase when you need random, realtime read/write access to your Big Data. This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google's Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, Apache HBase provides Bigtable-like capabilities on top of Hadoop and HDFS.

D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG and CSS. D3’s emphasis on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework, combining powerful visualization components and a data-driven approach to DOM manipulation.

Waarp provides a secure and efficient open source MFT solution.

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using a simple programming model. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-avaiability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-availabile service on top of a cluster of computers, each of which may be prone to failures.