DataGrip is JetBrains' cross-platform database IDE for relational and NoSQL systems. It gives developers, analysts, and DBAs one workspace to connect to databases, write and debug SQL, inspect schemas, edit data, and keep scripts under version control.
DataGrip is a standalone database IDE built by JetBrains for people who work with SQL and data every day. You create a data source, point it at a local, remote, or cloud database, and then work from a single window: browse schemas and tables in the database explorer, write statements in the query console, and run them in several modes at once. The editor understands your schema, so completion, navigation, and refactoring behave much like they do in a programming IDE. Results appear in an editable grid where you can filter, sort, and modify rows. Scripts, results, and local history stay together, so the tool fits naturally into an existing development workflow.
Beyond the editor, DataGrip doubles as a schema and data management environment. You can compare schemas, generate migration scripts, view relationships as diagrams, and export result sets to CSV, JSON, Markdown, or custom Excel templates. Version control is built in, so SQL scripts live in the same repository as application code while local history protects against accidental edits. Because data teams also juggle everyday desktop utilities, many readers arrive here from a general software guide such as What Adobe Acrobat Reader (32-bit) and stay for the database tooling. Remote connections, cloud warehouses, and several NoSQL stores are handled through the same connection dialog, and the objects you touch remain searchable from one place.
The clearest benefit of DataGrip is that it removes constant context switching between a database shell, a spreadsheet, and a text editor. Everything happens in one window, and because the editor understands the schema, mistakes tend to surface while you type rather than when a query hits a production table. Context-aware completion and quick-fixes shorten the loop between writing a statement and seeing a result, while on-the-fly analysis flags unresolved objects and probable bugs early. Refactoring tools let you rename or restructure objects safely across a project, and schema comparison helps keep development, staging, and production in step. Built-in version control keeps scripts auditable, and import and export options mean you rarely need a separate utility to move data between formats. Add consistent behaviour across relational, cloud, and NoSQL engines, and the result is a database environment that scales with a team instead of getting in its way. It is a paid tool for commercial work, though a free trial and a free non-commercial licence make evaluation straightforward.
Completion, navigation, and inspections read your table structure, foreign keys, and objects created in the code you are editing, so suggestions match the schema in front of you instead of generic syntax. That shortens the distance between an idea and a working statement.
Run a single statement, a selection, or an entire script, and keep several consoles open against the same or different data sources. Every query is written to a history log, so a useful snippet is easy to find again later.
Browse table contents in a grid that supports filtering, sorting, and direct cell editing. You can stage several row changes and commit or roll them back as one transaction, which is far quicker than hand-writing update statements for small corrections.
Load data from script files, CSV, TSV, or other delimited text, and export result sets to CSV, JSON, HTML, Markdown, Excel, or a template you define yourself. Delimited files can also be opened and edited as tables without a database connection.
The editor detects probable bugs and unresolved objects as you work and offers a fix directly in place. Catching a misspelled column or a mismatched type before execution saves a round trip to the server and avoids failed runs.
Compare two schemas to see exactly what differs, then generate a migration script from the result. Relationship diagrams give you a visual map of tables and keys, which helps when you are joining unfamiliar tables or briefing a colleague.
Unified support for major version control systems such as Git, SVN, and Mercurial keeps SQL files in the same repository as application code. Local history adds an independent safety net that records changes even when a file was never committed.
Relational databases, cloud warehouses, and several NoSQL stores connect through the same data source dialog with JDBC drivers, plus tunnel and SSL options for remote hosts. Teams that work across more than one engine avoid learning a separate client for each.
It is an integrated environment for working with databases: connecting to data sources, writing and running SQL, exploring schemas, editing table data, comparing structures, and moving data in and out of files. Developers, analysts, and database administrators use it as their main client instead of a mix of command-line tools and lightweight query utilities.
It covers widely used relational systems such as PostgreSQL, MySQL, MariaDB, Oracle, Microsoft SQL Server, and SQLite, along with NoSQL options including MongoDB and Apache Cassandra, plus cloud data warehouses and other engines reachable through JDBC drivers. Support is split between complete and basic driver levels, so it is worth checking the JetBrains documentation for the exact engine and version you rely on.
A free trial is available for evaluation, and JetBrains offers a no-cost licence for non-commercial use. Commercial use requires a paid subscription, which is available individually or as part of a JetBrains product pack covering multiple IDEs.
No. It is a standalone application, so you can install it on its own and work with databases without touching IntelliJ IDEA, PyCharm, or any other JetBrains product. The same database tooling is also available as a plugin inside several of those IDEs if you prefer to stay in one application.
Yes, to a large extent. The data grid lets you filter, sort, and edit rows directly, and the import and export tools move data between tables and delimited files or spreadsheets. Delimited files can be opened as tables and edited without any database connection at all. Hand-written SQL remains the deeper option when you need joins, aggregations, or complex transformations.
It supports the major version control systems, including Git, SVN, and Mercurial, so SQL scripts and migration files can be committed, diffed, and reviewed alongside application code. A separate local history keeps a timeline of edits to your files even before anything is committed.
Remote hosts and cloud platforms are configured as ordinary data sources, with optional SSH tunnels and SSL settings for encrypted connections. Practical performance still depends on network latency and on whatever access rules your provider enforces, so credentials and firewall permissions must be set up on the database side as well.
Yes. Schema comparison shows the differences between two structures and can turn them into a migration script, which you review and run yourself rather than applying blindly. Refactoring actions such as renaming objects propagate through dependent code, and version control keeps the resulting scripts reviewable.
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