Google - Google enables interactive Big Data analysis via a cloud platform with BigQuery. With an easy-to-use interface, this web-based architecture allows users to rapidly request SQL-like queries, and promptly access historical and real-time data. Companies with multiple positions and levels will appreciate the accessibility of separate interfaces, so that their users are provided with the data tools that they find necessary. Google provides flexible pricing for BigQuery, requiring payment only for the storage users need and the queries that they run.
MemSQL - Provisioning Big Data, MemSQL's database is structured as a tiered storage, which features an in-memory store and a flash or disk-based column store. The product allows data to be moved from memory to flash to disk, and effortlessly transfers data between storage engines. Users can run SQL queries as sophisticated as JOINS and work with JSON in a relational environment. The database also features ANSI SQL interfaces and uses MySQL client drivers, ODBC, and JDBC. MemSQL offers the option to house the data on premise, host in public, or via a private cloud
MongoDB - As a NOSQL open-source database, MongoDB prides itself as an agile and flexible platform fit for Big Data. The horizontal scaling product, MongoDB Enterprise, is an in-memory computing database. It enables JSON-like documents with versatile schemas to support instantaneous changes. Users are still equipped with the familiar components of traditional databases such as detailed consistency, comprehensive query language, and secondary indexes.