Why and how has SQL become an inevitable go-to language for Data Analysis?

Out of information analysis, comes wisdom. It’s this information analysis that created the need for storage of data. The world of databases was sprouted to address this need of data storage and many languages were born to interact and deal with the data stored in these databases. But one language has survived, stood the test of time, consistently evolved and thrived. SQL is that one language.

Databases space has undergone significant technological advances over the last couple of decades with innumerably distinct storage architectures. JSON databases, Key-Value pair databases, Document databases, HDFS and many other NoSQL databases are a few to name. These significantly distinct architectures lead to the creation of a number of database languages closely coupled to the underlying architectures, to interact and deal with the data stored in these databases. In the midst of stiff competition from these new languages, time and again SQL has survived, stood by the test of time and competition, continuously evolved since last four decades consistently ensuring relevance to the contemporary demands of data access, analysis and reporting and thrived to become the most inevitable go-to language for data analysis.

SQL is also closely coupled with relational database architectures, but it’s not the perfect implementation of the relational model. SQL deviates from relational model in considerably good number of ways. Simplest example is a Relation in strict sense is not expected to have duplicate records but a table (equivalent of Relation) in SQL can absolutely have duplicate records.

SQL is so much powerful yet flexible that the vendors of databases with non-relational storage architectures have also started implementing an abstracted layer of SQL on top of their respective non-relational database architectures under the umbrella named “SQL on Hadoop ecosystem”, without much compromising on the underlying database performance. Below are a few such SQL implementations:

HiveQL – an implantation of SQL layer on HDFS (Hadoop Distributed File System)

Impala – Cloudera Impala is Cloudera’s open source massively parallel processing (MPP) SQL query engine for data stored in a computer cluster running Apache Hadoop.

Presto – an open source distributed SQL query engine.

Apache Drill SQL – is an open-source ANSI SQL – 2003 compliant SQL framework that supports data-intensive distributed applications for interactive analysis of large-scale datasets.

What makes SQL apparently simple but so much powerful and flexible?

SQL is the 4th generation language that is declarative in nature. i.e., unlike procedural languages, SQL only expects you to mention what you want and never about how you want the database to work to achieve what you want. The declarative nature of SQL frees-up the user from worrying about technical complexities involved in indicating how to interact with data in the databases and lets the user focus on what is needed. It’s this declarative nature of the language that drives simplicity. SQL has consistently ensured relevance to the contemporary demands of data analysis and continuously evolved to today’s state with a compelling and rich array of analysis features and functions thus becoming powerful. The pure Relational model enthusiasts may not like to appreciate the fact that SQL deviates from its underlying Relational model considerably. But it’s this ability of SQL to deviate from its underlying model that resulted in its flexibility.

Its adoption at scale in the industry clearly shows that SQL has evolved from being just another database query language to a flexible and comprehensive framework for data analysis. This brings us to the conclusion and justifies that SQL is truly the most inevitable go-to language for data analysis and one of the most powerful tools in the arsenal of an analyst.

SQL has evolved from being just another database query language to a flexible and comprehensive framework for data analysis

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