The use of SQL query language is a matter of convenience. As long as the datasets can be enumerated, SQL can be used to query the data. This has been demonstrated by utilities like the LogParser. The universal appeal of SQL is that it is well-established and convenient for many. These not only include developers and testers but also applications  that find it easy to switch persistence layer when the query language is standard 

In fact, querying has become such an important aspect of any problem solving that analysts agree it contributes to the requirement for the right tool which is usually eighty percent of getting to a solution. The other 20% is merely to understand the problem we wish to solve.

To illustrate how querying is important, let us take a look at few topologies of organizing data at any scale. If we have large enterprise data accumulations with a data warehouse, whether it is on –premise or in the cloud, the standard way of interacting with it has been SQL.  Similarly, in-memory database scale out beyond single server to cluster nodes with communications based on SQL querying. Distributed databases send and receive data based on distributed queries again written in SQL.  Whether it is Social Engineering Company or a giant in online store, data marshaled through services eventually query based on SQL. Over the last two decades, the momentum of people adopting SQL to query data has only snowballed. This reflects in applications and public company offerings that have taken to cloud and clusters in their design.

Perhaps the biggest innovations in Query language has been the use of user defined operators and computations to perform the work associated with the data. These have resulted in stored logic such as the stored procedures which are written in a variety of languages. With the advent of machine learning and data mining algorithms, these have enabled support for new languages and packages as well as algorithms that are now available right out of the box and shipped with the tool.