SQL still leads, but newer query languages are rising fast in usage and demand
Graph-based tools like Cypher and GQL are vital for modern, connected data models
Developers now require multiple query languages to handle mixed data and systems efficiently
Query languages help software developers get the exact data they need from databases or APIs. Every type of data needs its own way of being asked questions, and that's where these languages come in. In 2025, with so many different kinds of data systems, developers are expected to know more than just SQL. Some of these languages are old but still important, while others are new and becoming popular quickly.
Here are the top 10 most relevant query languages for developers in 2025.
SQL remains the world's most utilized query language. It is used predominantly with relational databases, which organize data in rows and columns. After decades, SQL remains a key tool for developers, utilized in web application development, data analysis, app development, and backend solutions. In 2025, numerous businesses have also incorporated a new feature in SQL known as PGQ, which allows them to develop using graphs.
Also Read: Top Backend Programming Languages in 2025
GraphQL is often used to get data from web APIs. It lets the user decide what data to receive and avoids sending extra information. This saves time and makes apps faster. Frontend and backend developers now use GraphQL to connect websites and apps to databases more efficiently.
SPARQL is used in linked data and semantic web applications. It facilitates extracting data from knowledge bases such as Wikidata and DBpedia. SPARQL is utilized by universities, research teams, and businesses with extensive knowledge graphs.
Cypher is the main language for Neo4j, a popular graph database. Instead of rows and tables, Neo4j stores data in nodes and relationships. Cypher makes it easier to search through these connections. Companies use it for social networks, fraud detection, and recommendation systems.
Gremlin is a second graph query language, but it operates differently from Cypher. It focuses on paths and steps, almost like giving directions in a maze. It is utilized for extremely large or highly complex graph systems and is implemented in real-time applications as well as analytics software.
Also Read: Optimizing SQL Queries for High-Volume Data in PostgreSQL
PromQL is utilized in monitoring tools such as Prometheus. It assists developers in checking how their systems are performing. It can monitor memory usage, server loads, user activity, and other system metrics. PromQL is now significant in DevOps and site reliability engineering.
AQL stands for Arango Query Language. It is the query language of ArangoDB. ArangoDB is a unique type of database that can store documents, graphs and key-value data simultaneously. AQL allows developers to write a single query to handle various data types. This comes in handy for full-stack developers who create applications from scratch to completion.
Datalog is an older language, but it is still helpful today. It is good at writing rules and finding answers that require multiple steps. It is used in research, data analysis, static code checking, and security systems. Some newer tools like Rego and Datomic are based on Datalog.
GQL stands for Graph Query Language. It became an international standard in 2024. Its goal is to create a unified approach to writing queries across all property-graph databases. Developers expect that more databases will start supporting GQL in the next few years.
XQuery is used for querying XML data. It is still used in large companies and old software systems. JSONiq is similar but works with JSON, which is more common in new apps. These languages help in transforming and combining data from many sources.
Developers in 2025 need to work with different kinds of data, like tables, documents, graphs, and real-time numbers. Learning just one language is no longer enough. The ten query languages listed above are important in different situations. Knowing them helps developers work better with systems and solve problems faster. Some are simple and others are more advanced, but all are useful in today’s tech world.