What is query language?

Query language, which includes database query language (DQL), is a specialized computer language used to make queries and retrieve information from databases. It serves as an interface between users and databases, enabling users to manage data from a database management system (DBMS).

One of the most widely used modern query languages is Structured Query Language (SQL), a domain-specific programming language used to interact with relational databases and perform operations on structured data within. Structured query languages make use of commands like "FIND," "INSERT," "DELETE," "ORDER BY," "SUM," and "SELECT," which can be formed into syntactical statements for operations including defining and changing database schema, searching, populating and updating the contents of a database, and defining everything from triggers and integrity constraints to stored procedures and authorization rules.

In an increasingly data-driven world, query languages like SQL and other powerful variants are a foundational tool for data and computer professionals at every level.

In the broadest sense, a query refers to a request for data or information from a database or a data repository system. This request is typically made in the form of a specific question or command, written in a query language that the database understands.

A query can be as simple as fetching a specific subset of data from a particular table or as complex as performing intricate calculations and data transformations. Queries can be used to find, summarize, filter, combine, adjust, delete, insert, and update data. They can also answer data-related questions, analyze data from multiple tables at once, and automate data management tasks.

Some basic types of queries include select queries, parameter queries, make-table queries, append queries, and crosstab queries, but with the structured commands enabled by query languages, queries can also grow exponentially elaborate.

SQL is by far the most popular and commonly used query language for relational databases. It is known as a declarative language, meaning it describes what needs to be accomplished rather than how to accomplish it, but it also includes traditionally procedural elements.

Although there are many DQL and SQL variants, and the terms can overlap, SQL sub-languages are commonly divided into five main categories:

• Data Query Language (DQL)
• Data Definition Language (DDL)
• Data Control Language (DCL)
• Data Manipulation Language (DML)
• Transaction Control Language (TCL)

Additionally, there are languages for non-relational databases called NoSQL, or "Not Only SQL" databases. Unlike relational databases that use tables and structured data, NoSQL databases use methods like key values, graphs, wide columns, and documents to store and retrieve data. While SQL is suited for relational databases housing structured data and using predefined schemas, NoSQL excels with dynamic schemas for unstructured data and big data.

Just a few examples of database query languages, often developed for specific domains and use cases, include:

• DMX for data mining models
• MongoDB uses a method-based query format for data in MongoDB
• GraphQL for APIs
• Tutorial D for truly relational database management systems (TRDMS)
• XQuery for XML data sources
• Neo4j's Cypher for graphs
• CodeQL for automated security checks and variant analysis
• MDX for OLAP databases
• Elasticsearch Query DSL to access data in Elasticsearch

As data proliferates in nearly every corner of business and daily life, query languages are being deployed at an unprecedented scale. The depth and breadth of applications across domains and enterprise is limited only by the scale of database adoption and capacity worldwide.

From database administration to narrower use cases like business intelligence and software development, query languages are instrumental in enabling data-driven decisions. Through data mining and analytics, query languages are used to identify patterns and trends that can help everyone from customer service staff to CEOs gain valuable insights.

For large data-dependent companies all the way down to the smallest ecommerce merchants, the ability to leverage user information and act upon it is now a core pillar of many business models.

To name just a few practical applications across industries:

• In healthcare, DQLs can be used to analyze large data sets of information about patient behavior and medical conditions.
• Financial institutions from banks to payment processors use data query languages to do everything from detecting fraud to delivering personalized customer experiences.
• Social media platforms use DQLs to store, sort, and update information about user profiles, views, purchase history, new posts, likes and shares, and so much more.
• Marketers use DQLs and SQLs to identify target audiences, track sales attribution and campaign performance across channels, and analyze consumer behavior patterns.
• Database administrators use DQLs to update, maintain, and secure their environments, keeping confidential information about users, employees, students, or patients safe.
• Data analysts and data scientists in business and research use DQL to streamline the process of sorting through massive data sets to discover trends and insights.

Query languages and programming languages share some similarities, but they serve different purposes and operate in distinct contexts.

Purpose
The primary purpose of a query language is to interact or "talk" with databases and retrieve specific data. A programming language, however, is used to develop software applications, define algorithms, and manipulate data structures.

Syntax
Query languages like SQL have a predefined set of keywords and strict syntax tailored for querying databases, focusing on data extraction and manipulation. Programming languages have more general syntax, allowing for a broader range of functionalities and capabilities.

Use cases
Query languages are predominantly used to perform operations on databases. Programming languages are intended for a wider variety of applications, including developing software, web applications, and system software.

While SQL itself technically qualifies as a programming language, it is a domain-specific language built expressly to work with databases. Unlike SQLs, you can use General Purpose Languages (GPLs) to build entire applications and programs, with any type of logic you need.

Popular GPLs include Python, C++, Ruby, Java, and JavaScript, which you can use to write desktop, mobile, or web applications.

Though SQL is the most common query language, it's important to choose a language that aligns with your data goals. For example, different types of data are suited for different languages — like Cypher or Gremlin for graph databases or SPARQL for RDF data.

The best way to sharpen your QL skills is to regularly review and optimize your existing queries to improve their efficiency and performance. Utilize indexing, avoid unnecessary joins, and streamline your code to ensure your queries run smoothly and swiftly, even with large datasets.

Instead of sticking to simple "SELECT" statements, challenge yourself by working with more complex queries involving joins, left joins, subqueries, and aggregate functions. If you don't work with data query languages regularly, practice by tackling real-world scenarios that require you to retrieve specific data from multiple tables simultaneously.

Here are a few more tips to help you optimize your query language:

• Choosing the right join type can majorly impact query performance, allowing you to efficiently combine data from multiple tables.
• Indexes are a great tool for optimizing SQL queries, allowing the database engine to quickly locate and retrieve data based on specific columns.
• The structure of your database tables is crucial. Reduce the amount of data that needs to be processed to speed up queries.
• Subqueries allow you to nest one query inside another to retrieve more specific data.
• Use LIMIT and OFFSET keywords to retrieve a specific number of rows from a query, starting at a specific offset.
• Master the art of implementing stored procedures and functions in SQL. Automate repetitive tasks and complex operations by storing frequently used queries as reusable procedures. Query caching allows the database engine to store the results of oft-used queries in memory.
• Strive to learn advanced or unfamiliar SQL concepts like window functions, recursive queries, and common table expressions (CTEs). Understanding these advanced features can improve your ability to manipulate and analyze complex datasets and boost your overall proficiency.

There are numerous query languages available which we previously discussed; each offers unique strengths that can streamline and expedite your objectives with efficiency and ease.

Elasticsearch Query Language (ES|QL) is a new piped language that transforms, enriches, and simplifies data investigations. Powered by a new query engine, ES|QL delivers advanced search capabilities with concurrent processing, improving speed and efficiency irrespective of data source and structure. ES|QL can be used to quickly search and aggregate data to easily find what you are looking for.

ES|QL is more than a language. It signifies a major shift in the approach to data investigations within security and observability. Rather than transpiling query expressions to Query DSL for execution, ES|QL search, aggregation, and transformation functions are directly executed within Elasticsearch itself. Delivering an iterative language enables analysts to easily ask questions to greatly simplify investigations.