Big Data SQL

SQL is a query language for big data. With big data, SQL is a great tool.

SQL was developed in the 1970s by two IBM researchers. It was created to solve the issue of how to deal with large data sets and how to organize it in a way that made it accessible. In 1986, the American National Standards Institute (ANSI) adopted SQL as a standard, which is known as the ANSI SQL standard. Even after all this time, SQL is still growing in popularity. 

SQL is the most common way to work with any database. It is easy to learn and operates with the majority of databases. Adherence and support of the ANSI SQL standard has been an important characteristic for a federated system like open source Presto, a SQL query engine. While there are variants and extensions for SQL, to be compliant with ANSI SQL means that the major, commonly-used commands, like SELECT, UPDATE, DELETE, INSERT, and WHERE, all operate as one would expect. Therefore, SQL code that works on other databases will likely work on Presto without any changes to the SQL and dozens of popular Business Intelligence (BI) tools work with the Presto engine without any additional integration. Since most users already know how to write SQL, Presto is easily accessible, and doesn’t require any further learning. Because Presto is SQL compliant, it immediately enables a large number of use cases

SQL is commercially supported on all major cloud platforms and most developers are familiar with it in some form due to its prevalence in the industry. For cloud providers like AWS, Google Cloud, and Microsoft’s Azure, SQL database instances are charged per hour (with additional costs for backup) based on the size of the instance and any other provisioning that is needed.