watson-machine-learning-client is a library that allows to work with Watson Machine Learning service on IBM Cloud. Train, test and deploy your models as APIs for application development, share with colleagues using this python library.

See also https://pypi.org/project/watson-machine-learning-client/

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.

Apache Cassandra is an open-source, NoSQL, wide column data store that can quickly ingest and process massive amounts of data. It’s also decentralized, distributed, scalable, highly available, fault-tolerant, and tunably consistent, with identical nodes clustered together to eliminate single points of failure and bottlenecks (we’ll go over each of those terms later). You can deploy Cassandra on-premise, in the cloud, or in a hybrid data environment.

More info http://cassandra.apache.org/

MongoDB is a general purpose, document-based, distributed database built for modern application developers and for the cloud era.

More info https://www.mongodb.com/

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. 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-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.