DataGrip is a database IDE for SQL and NoSQL work that connects to many engines at once, offering schema-aware completion, a data grid, refactoring, import and export in many formats, and version control for the scripts around a database project.
DataGrip is a client for relational and NoSQL databases, built so that one application handles PostgreSQL, MySQL, SQL Server, Oracle, MongoDB, Redis, ClickHouse, and many others. Add a connection, browse the schema tree, and the editor knows the columns, keys, and routines involved, which turns completion and navigation into something closer to writing code in an IDE. Queries run in consoles with local history, results open in an editable grid, and data moves in and out through import and export dialogs. AI assistance, diagrams, and schema comparison round out the workflow. Connections can also be tunneled over SSH, which matters when the database is not directly reachable.
DataGrip suits developers, analysts, and database administrators who work across several systems rather than specializing in one vendor's tool. Analysts like the export options and the editable grid for quick corrections, developers appreciate refactoring and version control for migration scripts, and administrators use schema comparison when promoting changes between environments. Keeping every connection in one window removes the habit of hopping between vendor clients with different shortcuts. It deals with stored data rather than the traffic between services, so a network-level view of those requests belongs to a different tool, such as Progress Telerik Fiddler Classic. Teams typically keep it open all day for exactly that reason.
DataGrip pays off in the number of small interruptions it removes. Completion that knows the schema means fewer trips to documentation to check a column name, the data grid means a quick correction does not need a hand-written UPDATE statement, and query history means an ad-hoc report from last month can be found and rerun in seconds. On the structural side, refactoring follows every reference to a table or column, schema comparison shows exactly what differs between two environments before a release, and diagrams make an unfamiliar database readable. Import and export cover the moment a colleague asks for data in a spreadsheet, and version control keeps scripts beside the rest of the codebase. The cumulative saving is hard to measure but easy to feel across a working week.
Key capabilities and highlights.
Relational engines, cloud warehouses, and NoSQL stores are supported side by side, so a project can include a production database, a reporting warehouse, and a cache in the same window. Connections can be tunneled over SSH and labeled for quick identification.
DataGrip suggests based on the actual schema, including tables, columns, foreign keys, and objects created earlier in the same script. That reduces typos in join conditions and makes long analytical queries faster to assemble, even in a database with hundreds of tables.
Queries run in consoles with configurable execution modes and parameters, and every statement is written to a local history log. That makes it easy to recover a query from weeks ago rather than reconstructing it from memory.
Results open in a grid where values can be changed, rows filtered, and columns hidden, with changes submitted as statements after review. Correcting a single bad value no longer requires writing an UPDATE statement by hand, and the edit is visible before it is committed.
DataGrip can import data from CSV, TSV, and delimiter-separated text files, and export it to CSV, JSON, HTML, Markdown, Excel, or a custom format. That covers most of the requests that arrive from colleagues outside the database team.
Unresolved objects, likely mistakes, and dialect problems are flagged as the script is written, with an offer to correct or create the missing object. Catching a misspelled column before execution saves a round trip to the server.
Renaming a table or column updates every reference, schemas can be compared between two databases to see what differs, and diagrams render relationships as a readable map. Together these turn a deployment check into a visual review instead of a manual one.
An AI assistant can draft queries, explain unfamiliar SQL, translate between dialects, and suggest performance fixes, using the schema as context. Scripts also live under version control, so database changes are reviewed alongside application code rather than passed around informally.
Browse historical releases and previous builds.
Common questions about DataGrip.
It is a database client and SQL IDE used to connect to databases, explore schemas, write and run queries, edit data, and move data in and out through import and export. It supports relational engines, cloud warehouses, and NoSQL stores in the same application.
It covers the major relational systems, including PostgreSQL, MySQL, MariaDB, Oracle, SQL Server, SQLite, and Db2, along with cloud options such as BigQuery, Snowflake, and Redshift, plus NoSQL stores such as MongoDB, Redis, Cassandra, and Couchbase.
Yes. Query results open in a grid where values can be edited, rows filtered, and columns sorted or hidden, and the pending changes are shown as statements before they are applied. That suits fixing a single record without writing SQL by hand.
Schema comparison shows what differs between two databases, which is useful before a release, and refactoring updates references when a table or column is renamed. Diagrams and version control for scripts make those changes easier to review with the rest of the code.
Yes. The assistant built into DataGrip works with the schema as context, so it can draft queries, explain SQL that you did not write, convert between dialects, and suggest optimizations. Suggestions remain reviewable before anything is run against a live database.
DataGrip is a database IDE for SQL and NoSQL work that connects to many engines at once, offering schema-aware completion, a data grid, refactoring, import and export in many formats, and version control for the scripts around a database project.
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