Nowadays, huge volumes of unstructured data are generated, and this cannot be restricted to standardized text inputs for the RDBMS. For example, every corporate entity has applications that generate ...

Relational database management systems (RDBMS) rely on an optimizer (or relational optimizer) that transforms SQL statements into executable code. Before any SQL statement can be run by the RDBMS, the ...

The rise of new data types is leading to new ways of thinking about data, and newer data storage and management technologies, such as Hadoop, Spark, and NoSQL, are disruptive forces that may ...

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Even though MongoDB and Cassandra keep winning converts, enterprises are keeping their RDBMSes around, and will do so for quite some time. NoSQL promised to upend the database market as big data ...

Last issue I talked about Thor Technologies, its new round of funding and its “best of breed” solutions. But what the Thor folks really wanted to talk about was the use of SQL-based relational ...

The motivating idea behind Codd's relational model wasn't data storage at all. Shared data storage was, to him, merely a means to an end: efficient, easily developed and debugged, applications.

Many people associate open source data framework Hadoop with managing truly massive amounts of data. And with good reason: Hadoop storage is used by Facebook and Yahoo, which many people (rightly) ...

A database that maintains a set of separate, related files (tables), but combines data elements from the files for queries and reports when required. The concept was developed in 1970 by Edgar Codd, ...