There are a few data languages and processes. We'll break them down here:
SQL is a database querying language specialized for use in managing relational database systems and stream processing in relational data stream systems. Well-known extensions of the language include MySQL, NoSQL, PostgreSQL, and PL/SQL.
mySQL is a open source relational database management format based on SQL and is used heavily in software packages and web applications that integrate the LAMP (Linux, Apache, MySQL, Python/Perl/PHP) software stack.
PostgreSQL is an extension of SQL used in object-relational database management systems. One major usage for this format is storing and distributing sensitive data in compliance with federal regulations and industry best practices, as security and standard-compliance are key design factors incorporated in the format.
Hive is an programming infrastructure based on Hadoop used for data summarization, query, and analysis, making it a common tool used in data mining and warehousing applications.
Hadoop is an open source computing framework used for storing and processing large distributed data sets. Its operation is based on commodity computing, which is considered more cost-effective than utilizing centralized supercomputers, and as such, makes Hadoop heavily used in computing operations on Big Data.
Spark is an implementation of the Ada language used heavily in mission-critical applications. The language addresses certain issues with Ada to emphasize reliability, power, speed, security, bounding of required resources, and consistency. As such, the language is used primarily in safety and security systems, as well as systems to sustain critical business operations.
XML, or Extensible Markup Language, is a language used for encoding documents in human-readable and machine-readable formats. Like JSON, APIs can parse and process XML data into valid data used for applications.