Query files directly.
Explore local CSV, Parquet, and JSON files with SQL, right inside DataWorx.
BETA · BUILD 178
Try the DataWorx beta
The beta is free to try. It's a time-locked beta: it stops working after October 31, 2026. After that it shows a notice with a link back to this page, where a fresh build will be waiting.
New in this build: View Data handles huge values. Selecting a cell that holds megabytes of text, such as file data stored in a JSON column, no longer crashes DataWorx. View Data shows the first 100,000 characters, and Copy still copies the whole value.
From the previous build: copy a table anywhere. Right-click a table and choose Copy Table… to make a new table with the same columns, keys, indexes and rows under a new name, in the same database or on another connection, even another kind of database: types are translated for you, and a copy that fails is removed again. Tables copy into MongoDB as collections, and collections copy back out as tables. MongoDB arrived three builds ago: see what it does ↓
Download for Apple Silicon ↓DataWorx-0.1.0-178.dmg · 35 MB · macOS 11 or later on an Apple silicon Mac (M1 or newer). This build doesn't run on Intel Macs.
What installing looks like
- Open the downloaded disk image and drag DataWorx into Applications.
- This beta isn't notarized by Apple yet, so the first time you open it, macOS says it can't verify DataWorx. Click Done, not Move to Trash.
- To let it open, run this once in Terminal (it removes the "downloaded from the internet" flag from DataWorx):
xattr -dr com.apple.quarantine /Applications/DataWorx.appThen open DataWorx again. From then on it opens normally. - DataWorx keeps saved connection passwords in your keychain. The first time it opens a saved connection, macOS asks for your keychain password. Enter it and click Always Allow.
Windows: The Windows build is being tested now and will be available here this week. A Linux build may follow.
THE DESKTOP DATABASE WORKBENCH
Your databases.
Your workflow.
Serious database work. One complete workspace.
Write SQL, design schemas, debug routines, chart results, and work with AI.
Built in Rust. FAAAASSSST by design. Native compiled performance and efficient resource use keep your database workflow responsive.
Built by a software developer with four decades of database and software development experience.
9 connection types241 documented capabilities$50 perpetual release license
AT HOME WITH YOUR DATABASES
PostgreSQLMySQLMariaDBSQLiteSQL ServerOracleSnowflakeMongoDBODBC
See upcoming connections ↗INTEGRATED NOW / DUCKDB
Local files.
Connected data.
One SQL workspace.
DuckDB is already integrated into DataWorx. Its embedded analytics engine turns your database workbench into a place to work with data wherever it lives.
Bring results together.
Join PostgreSQL results with Snowflake results or a file on your machine through the integrated DuckDB engine.
Stay in your workflow.
Work with files and database results in the same workspace, with DuckDB built in and ready for analysis.
NEW NOW / MONGODB
Native queries.
Guided pipelines.
Real documents.
MongoDB is now part of DataWorx: write MQL just as you would in the MongoDB shell, with an editor that knows your collections and fields and helps you build every stage of an aggregation.
Write MQL, not workarounds.
find, aggregate, inserts, updates and indexes, exactly as the shell takes them. Autocomplete suggests collections, the fields in your documents, and the $ operators that fit where you are.
Pipelines that guide you.
Choose a stage and get its whole phrase with placeholders to Tab through. See every form of the operator you're writing. After a $group or $lookup, the fields offered are the ones that exist at that point.
Documents, your way.
Results open as MongoDB JSON, or as a table with a column per field. Edit sub-documents as JSON, without drilling down; types are kept and conflicting changes are never overwritten.
COMING SOON / DATABASE CONNECTIONS
More databases.
The same DataWorx focus.
Our next connection tracks bring more cloud platforms, analytics engines, and document stores into your workspace. MongoDB, the first of the NoSQL track, is already here.
01 / POSTGRESQL FAMILY
Familiar roots.
New destinations.
- Amazon Redshift
- CockroachDB
- TimescaleDB
- Supabase / Neon
Purpose-built connections on the PostgreSQL protocol foundation, with dialect and catalog refinements for each platform.
02 / ANALYTICS & CLOUD
Go where the
analysis takes you.
- ClickHouse HTTP or native protocol
- Google BigQuery REST API connection
Dedicated connections for interactive analytics and cloud-scale queries. DuckDB’s embedded engine is already integrated into DataWorx.
03 / A DEDICATED NOSQL TRACK
Native queries.
Native data panels.
- MongoDB available now
- Other NoSQL stores
Document and collection explorers, native query consoles, and native data display panels designed around each store’s own query language and data model.
Explore collections. Write native queries. View documents in purpose-built panels.
WINDOWS & LINUX / IN TESTING
Help shape DataWorx
on your platform.
Windows and Linux versions are in testing, and we’re actively seeking testers. Put DataWorx through your everyday database workflows and help us refine the desktop experience on both platforms.
TESTERS WANTEDUse Windows or Linux?
Use Windows or Linux?
We’d love your feedback.
Test real queries, connections, and workflows on the platform you use.
Become a tester ↗BUILT FOR THE WORK YOU DO
A workbench with
serious depth.
A complete workflow, from connecting to a database to sharing the answer.
SQL that keeps up with you
Contextual completion, dialect-aware highlighting, formatting, and name inspections keep your thoughts moving. Save reusable queries with tags and Git-backed folders.
Get hands-on with your data
Explore results in a flexible grid. Sort, search, transpose, and inspect JSON or XML. Stage edits, review them, then submit the batch with transactional conflict detection.
See the structure behind it
Design tables visually, preview the SQL, and compare table structures across connections. Explore foreign-key relationships in interactive ER diagrams.
Turn results into answers
Chart query results as bars, lines, scatter plots, or pies. Inspect execution plans and actual query timings to understand how your SQL runs.
AI, on your terms.
AI is available, but DataWorx will never force it on you. Use it when it helps, or work entirely with the database tools you know. Chat with your choice of AI engine using SQL and schema context. Connect external AI clients through MCP, with explicit connection access and read-only defaults.
Move data with purpose
Import CSV and TSV, dump and restore with database-native tools, generate realistic test data, and export to Excel, JSON, SQL, Markdown, HTML, XML, or custom delimited formats.
◈
Your connections, thoughtfully handled.
System keychain credentials, SSH tunnels, TLS settings, and connection colors that help you know exactly where you’re working.
THE FULL PICTURE
Depth in every direction.
241 documented capabilities across 13 areas. Explore the complete DataWorx feature catalogue.
Browse all 241 capabilities below. Use your browser’s Find command to locate a feature.
01
Database connectivity
17- PostgreSQL
- MySQL
- MariaDB
MySQL protocol adapter
- Oracle
Direct Rust protocol client for database connections.
- Microsoft SQL Server
TDS client
- SQLite files
open existing or create missing file
- Snowflake
HTTPS SQL API
- MongoDB
Official driver; host and port or mongodb+srv:// connection strings, TLS and SSH tunnels.
- Generic ODBC connections
DSN, driver, or connection string
- Discover installed ODBC drivers and data sources
configuration-file discovery
- Save, edit, duplicate, delete, and test connections
all nine connection types
- Connection names and identifying colors
- PostgreSQL connection URL import
fills dialog from a PostgreSQL URL
- SQLite lock timeout and create-if-missing controls
- Bundled unixODBC driver manager
Connect with installed database-specific ODBC drivers.
- Snowflake account, warehouse, database, and role settings
Choose the context for your cloud data workflow.
- SQLite attached-database browsing
Explore main and attached schemas.
02
Authentication, networking, and session settings
24- Store passwords outside connection JSON
system keychain
- PostgreSQL .pgpass authentication
default, environment, or chosen file
- MySQL/MariaDB option-file authentication
.my.cnf and applicable option groups
- MySQL encrypted login-path authentication
.mylogin.cnf
- Snowflake token, key-pair, and OAuth authentication
token/key passphrase can be stored securely
- Read/import Snowflake connections.toml profiles
- Oracle tnsnames.ora aliases
- SQL Server Kerberos sign-in
macOS GSSAPI ticket
- Retrieve connection passwords from your password manager or secrets service
includes a Test action
- Passwordless connection option
Use database trust or certificate authentication.
- SSL/TLS configuration
Configure secure connections using your database driver’s TLS options.
- PostgreSQL CA and client certificate/key settings
- PostgreSQL direct TLS, channel binding, and target-session controls
- PostgreSQL Unix sockets and multiple failover hosts
- SSH jump-host tunnel
PostgreSQL, MySQL, MariaDB, Oracle, SQL Server
- SSH agent, private key, or password authentication
agent can include 1Password
- SSH host-key verification
known_hosts
- Read-only sessions
Select read-only session settings for supported database backends.
- SQL initialization on new sessions
- Configurable row limits
table-open and maximum-fetch limits
- Show/hide system schemas and additional databases
template-database option for PostgreSQL
- Application name, connection timeout, keepalives, startup options
principally PostgreSQL settings
- SSH secrets from keychain or password commands
Resolve jump-host passwords and key passphrases securely.
- Connection testing with tunnel diagnostics
Identify jump-host and database connection status separately.
03
Explorer, metadata, and SQL consoles
22- Hierarchical connection/database/schema/object explorer
loads on expansion
- Tables, views, materialized views, foreign tables, sequences, routines, and types
Explore the objects provided by each database backend.
- Inspect columns, keys, indexes, and CHECK constraints
grouped under tables
- Explorer filtering, refresh, and collapse
- Choose which databases a connection lists
Tick the ones to show, or hide one from its menu; a count like (2 of 5) by the connection opens the choice again. Oracle connections choose schemas.
- Open table rows and count rows
- Copy object name or qualified name
- Show generated/retrieved object DDL
Inspect database-specific object definitions.
- New SQL consoles and multiple query tabs
can start without a connection
- Switch console connection/database
- Execute selected SQL or entire buffer
- Multi-statement scripts
final result set displayed
- SQL Server GO and Oracle PL/SQL script delimiters
dialect-aware splitting
- Cancel a running query
Server-side cancellation through the database adapter.
- Statement row counts, elapsed time, and server messages
Execution feedback and supported server notices in the Output tab.
- Close current/other/left/right tabs and switch tabs by keyboard
- Trigger browsing and detailed column metadata
Inspect identity/computed attributes and database comments.
- Connect and disconnect from explorer menus
Manage your sessions alongside database objects.
- Reorder connections
Drag a connection into place, or use Move Up / Move Down; the order is kept.
- Generated table queries
Open tables with dialect-appropriate SELECT SQL and configurable row limits.
- Persistent query-session state
Keep SQL Server temporary tables, SET settings, and USE context between runs.
- Standard editor commands
Undo, redo, cut, copy, paste, and select all.
04
SQL editing and saved queries
23- SQL syntax highlighting
dialect-aware
- Contextual schema/table/column/alias completion
automatic or explicit invocation
- Keyword and common-function completion
- Refresh completion metadata after schema changes
- Format selection or entire console
- Configure formatter keyword case and indentation
- Preserve literal/comment content and support formatting undo
token validation before replacement
- Flag unresolved tables, columns, and aliases
conservative scope-aware checks
- Resolve CTEs, aliases, nested queries, and correlated references
Scope-aware name resolution across SQL statements.
- Highlight symbol usages
aliases, CTEs, tables in statement
- Rename aliases and CTEs with usage updates
F2/context menu
- Disable name inspections
- Save and reopen SQL associated with a connection
- Named query library with tags, descriptions, search, and tag filters
- Open a saved query with or without execution; update or save as new
- Saved queries as individual SQL files
Choose a folder; names, tags, descriptions, and connection context travel in comment headers. Individual SQL files make Git versioning straightforward: track changes, review diffs, and restore earlier versions of your queries.
- Move between folder and local-library storage
Migrate your query library in either direction.
- Discover existing and newly added SQL files
Bring your own query collection into the sidebar.
- Live reload of external SQL changes
Pick up edits and Git pulls within seconds.
- Portable connection matching
Match saved-query connections by name on another computer.
- Git branch and file status in the sidebar
See new and modified queries alongside their branch.
- Commit query changes from DataWorx
Keep query work in version control without leaving the app.
- Copy, edit, and organize saved queries
Manage SQL and query metadata from the sidebar.
05
Results, value viewing, and data editing
22- Scrollable result grid, cell selection, keyboard navigation
virtual scrolling
- Resize/auto-fit result columns and sort by header
client-side sorting
- Distinct NULL display and typed value presentation
- Find result text with case, whole-word, and regex options
- Filter visible columns by name
case/whole-word/regex options
- Transpose rows and columns
selection/editing follows layout
- Copy cells, rows, or all fetched rows
TSV/CSV options
- Full-value viewer/editor
floating or docked
- Detect, format, and color JSON/XML values
- Inline result-grid editing across all eight connection types
Edit single-table query results containing the full primary key.
- Stage inline edits, submit together, or revert
Review highlighted pending changes before submitting the batch.
- Multiline editing, Set NULL, and revert in value viewer
Work with full values in editable table results.
- Match updates using primary-key columns
full key must be present in editable results
- Transactional updates with conflict detection
Every update must match exactly one row; conflicting batches roll back together.
- Table/alphabetical column order and primary-key-first option
grid, copy/export, explorer
- Configurable value-type colors, numeric alignment, local timestamp display
separate light/dark colors
- Refresh while preserving your grid layout
Retain sorting, column widths, and scroll position on eligible read queries.
- Clear displayed results
Reset the result area from its toolbar.
- Data-aware initial column widths
Fit headers and values automatically with practical limits for long text.
- Database-aware edit targeting
Save edits to the database identity associated with the result.
- Case-aware table matching
Respect the database’s identifier case rules.
- Pending-edit protection
Keep pending changes intact when using refresh and clear controls.
06
Schema design and comparison
16- Visual create/modify table designer
database-specific SQL
- Edit column names/types/nullability/defaults/comments
Generate SQL appropriate for the connected database.
- Primary keys and auto-numbering for new columns
- Unique and foreign keys, referenced columns, referential actions
Configure database-specific keys and ON DELETE / ON UPDATE actions.
- Indexes and CHECK constraints
- Quick add-column/index/key dialogs from explorer
- Unique/partial indexes; PostgreSQL method and concurrent creation
Database-specific index options
- Preview generated CREATE/ALTER SQL before applying
execute or open in console
- Drop table/column/index/key with SQL confirmation
Review the database-specific SQL before applying the change.
- Transactional SQLite table rebuilding
Copy rows and swap the rebuilt table in a transaction.
- Compare two tables' columns, indexes, and keys
side-by-side differences
- Compare tables across connections
- Match differently named indexes/keys by their columns
- Preserve SQLite triggers during rebuilding and renaming
Recreate triggers transactionally with the updated table.
- Trigger-aware schema change guidance
Plan related column and trigger changes in the table designer.
- Refresh metadata after schema changes
Keep explorer and SQL completion current.
07
Execution plans and PostgreSQL debugging
11- Explain selected statement or statement at cursor
- Plan tree with operations, relation/index names, rows, costs
PostgreSQL, MySQL/MariaDB, SQLite, Oracle, SQL Server, Snowflake
- Highlight expensive operations and show condition details
cost/time-share bars and tooltips
- Explain Analyze with actual rows, loops, timings
PostgreSQL, MySQL/MariaDB, SQL Server
- Flag cardinality estimates off by more than 10×
- Transactional PostgreSQL Explain Analyze
Run analysis inside a transaction and roll it back afterwards.
- View raw database plan output
- Interactive PostgreSQL PL/pgSQL debugger
Step through routines with the PostgreSQL debugger extension configured.
- Debug arguments, breakpoints, step into/over, continue
- View/change variables and inspect call-stack frames
- Show debugger call result, messages, and setup guidance
can create extension after server prerequisites
08
ER diagrams and result charts
13- ER diagrams for schema, table and related tables, or selected tables
- Show columns, key markers, and foreign-key relationships
- Include referenced tables from other schemas
distinguished with dashed outlines
- Automatic layout and draggable table positions
orthogonal relationship routing
- Pan/zoom, find table, control displayed columns
- Highlight relationships and open table data from diagram
- Export ER diagram to SVG/PNG
- Chart query results
separate Chart tab
- Bar, line, scatter, and pie chart types
- Choose X column and multiple numeric series
- Automatic chart choice and sum repeated categories
- Hover values and navigate chart by zoom/pan
- Save chart as PNG or copy image
09
Import, export, and generated test data
31- Import CSV/TSV into existing table or create a new table
- Copy a table under a new name, here or to another connection
columns, keys, indexes, and rows; types translated between databases; undone if it fails
- Detect/configure delimiter, header, and encoding
UTF-8, UTF-16, Latin-1 fallback
- Map or skip source columns and infer new-table types
- Prevalidate values and NOT NULL, report file line numbers, skip bad rows
- Import transaction and progress display
Transactions handled by the connected database
- Export CSV/TSV and custom delimited formats
- Export typed JSON
- Export SQL INSERT and primary-key-based UPDATE statements
- Export Excel .xlsx
typed cells, frozen header, filters, fitted widths
- Export Markdown and HTML tables
- Export XML
- Preview export and choose file or clipboard
Format-appropriate clipboard output
- Copy HTML as rich table and Excel output as TSV
- Save custom CSV formats with quoting, escaping, NULL, row/value separators
add/duplicate/reorder/delete presets
- Database dump and restore
PostgreSQL, MySQL, MariaDB, SQLite, SQL Server via SqlPackage, and Oracle via Data Pump.
- Generate realistic test rows through a table UI
up to 100,000 rows/run
- Per-column generators chosen from names/types and editable by user
- Names, emails, phone, username, company, address, country, postal code
- Numeric/date/time ranges, booleans, UUIDs, URL, words, titles, codes
- Sequence, fixed, list, NULL, and database-default generators
- Reuse foreign-key values and generate distinct key values
references existing rows
- CHECK-aware generation and configurable NULL proportions
Generate values for supported lists, ranges, and length rules.
- Preview generated rows and insert transactionally
- Open generated INSERT statements in SQL console
up to 1,000 rows
- Database, schema, and table dump scopes
Choose supported scopes in the database dump workflow.
- Structure-only, data-only, or combined dumps
Select supported output content options.
- PostgreSQL archive formats
Custom, plain SQL, directory, and tar formats.
- Database-native restore tools
Use pg_restore/psql, mysql/mariadb, sqlite3, SqlPackage, or Oracle impdp as appropriate.
- Tool discovery and configurable tool folders
Choose the database command-line tools for your installation.
- Dump and restore progress, output, and cancellation
Manage long-running operations from a dedicated window.
10
AI assistants, MCP, and file access
22- Integrated AI chat with SQL/schema context
- OpenAI and OpenAI-compatible endpoints
- Anthropic, Gemini, and Ollama configuration
dedicated choices, Gemini/Ollama use compatible APIs
- Multiple configured engines and model selection
one chat tab per enabled engine
- AI inspect console/schema, write SQL, execute, and inspect results
- AI switch the target connection
- Optional confirmation before AI SQL execution
- AI key storage in system keychain
- Resizable prompt box and prompt-history navigation
up/down history
- Expose database access to external AI clients using MCP
Connect through DataWorx --mcp using stdio.
- Explicitly enable individual connections for MCP
hidden otherwise
- MCP read-only default, optional write permission, bounded results
per connection, max 5,000 rows and bounded response
- MCP list connections/databases/schemas/objects, describe tables, retrieve DDL
- MCP run SQL and compare tables
- MCP list/read saved queries
- AI list/read/search explicitly shared local folders
internal chat and MCP
- Optional AI write/move/delete permission per shared folder
off by default, paths resolved inside allowed roots
- Dedicated AI console context
Give assistant SQL work its own console context.
- Shared-folder metadata and folder creation
Inspect files and create folders within explicitly writable shared locations.
- Symlink-aware shared-folder boundaries
Keep file operations inside the roots you share.
- Connection password protection in AI responses
Keep connection passwords out of assistant output.
- Reload MCP connection settings on each call
Pick up shared connection changes without restarting the server.
11
Appearance and desktop workflow
21- Light, dark, or system appearance
- Comfortable/condensed interface spacing
- Select interface and editor/grid fonts and sizes
installed-font search
- Keyboard font zoom/reset
- Built-in SQL editor color themes
17 including Pastel Night
- Independently select light/dark editor themes
- Import VS Code color-theme JSON directly
persisted locally
- Color wheel and named-color palette
connection and data colors
- macOS native menu bar and standard platform shortcuts
in-window menus on other platforms
- Separate settings/connection windows and native destructive alerts
- Start page with connection/query actions and recent saved queries
- In-app What's New with unread release tracking
- About/build information and issue/feature-report actions
version, build, commit, email actions
- Persist local settings, connections, formats, and query library
JSON and environment-variable path overrides
- Theme previews and imported-theme management
Preview editor palettes and remove imported themes in Settings.
- Workspace panel shortcuts
Toggle explorer, saved queries, AI, value viewer, and transposed results.
- PostgreSQL documentation from Help
Open database documentation from the app.
- Searchable third-party license notices
View bundled component licenses and copyright details in About.
- Atomic configuration saves
Save connections, preferences, query JSON, and themes using temporary-file replacement.
- Automatic configuration backups
Keep the previous configuration version in a .bak file.
- Configuration recovery at startup
Preserve damaged configuration and restore an available backup.
12
Engineering and release foundation
9- Rust and egui desktop architecture
A compiled desktop application with native platform integration.
- macOS app bundles and DMG packaging
Drag-to-Applications distribution.
- Apple silicon and optional universal builds
Build tooling for Apple silicon and Intel.
- Developer ID signing and notarization workflow
Release tooling supports signing, hardened runtime, notarization, and stapling when configured.
- Embedded build and release metadata
Version, build, commit, and release-note stamping.
- Database integration, UI, and module tests
A test suite spanning application rendering, adapters, and workflow logic.
- Sample databases and SQL examples
SQLite samples, container-based database setup, and mock AI/Snowflake services.
- Database adapter compatibility work
Vendored Oracle, SQL Server, and unixODBC integration.
- Bundled dependency license notices
License-generation tooling and searchable attribution inside the app.
13
MongoDB
10- MQL consoles
Query MongoDB as you would in the MongoDB shell: find, aggregate, inserts, updates, indexes, show and use.
- MQL autocomplete
Collections, methods, fields sampled from your documents, and the $ operators that fit where you are.
- Whole phrases with placeholders
Choosing an operator inserts its complete phrase; Tab moves from one placeholder to the next.
- Syntax at a glance
Every form of the operator or method you're writing, with the key you're on explained.
- Guided aggregation pipelines
Fields follow the pipeline through $group, $project and $lookup; $lookup offers the other collection's fields.
- MQL formatting and error marks
Beautify long queries and see where a statement stops making sense.
- Documents as JSON or a table
Results open as MongoDB JSON; the table view shows sub-documents as JSON to edit in place.
- Type-safe document edits
Edits keep each value's BSON type, are written by _id, and never overwrite someone else's change.
- Collections, views, fields and indexes
Browse each database's collections with the fields found in their documents.
- Copy collections to SQL tables, and tables to collections
a row per document, typed values, _id as the key, indexes kept
Database-specific capabilities are named in the catalogue. This is the DataWorx product feature inventory; edition licensing is shown below.
REAL APP. REAL WORKFLOWS.
Meet your next workspace.
Captured from DataWorx running with a sample database.
DEPTH YOU CAN PUT TO WORK
Powerful tools.
Thoughtful safeguards.
Robustness lives in the details: how changes are applied, how credentials are handled, and how your workspace is preserved.
Review. Submit. Commit together.
Stage grid changes before saving. Each update must match exactly one row, and conflicting or failed batches roll back together. Primary-key matching and database-aware result identity keep updates grounded in the data you read.
See the SQL before it runs.
The table designer previews database-specific CREATE and ALTER statements. SQLite rebuilds copy rows and swap tables transactionally, preserving triggers as part of the change. Explorer and completion metadata refresh afterwards.
Use the credentials you already trust.
System keychain storage, external password commands, pgpass, MySQL login paths, Snowflake tokens and keys, Oracle aliases, and SQL Server Kerberos support meet existing authentication workflows. SSH tunnels verify jump-host keys.
Share exactly the context you choose.
Enable MCP per connection, start with read-only access, and choose write permission explicitly. Results are bounded. Shared folders have separate permissions and symlink-aware root boundaries. Optional confirmation puts you in charge of assistant SQL execution.
Configuration with a recovery path.
Connections, settings, saved-query JSON, and imported themes use atomic saves. Previous versions are backed up automatically. Startup recovery preserves damaged files and restores available backups.
Built and tested as a desktop app.
Rust and egui power the application. Module tests, UI rendering tests, database integration scenarios, and mock service tests exercise its workflows. The screenshots on this page come from running the actual application against sample data.
8database connection types
100,000generated test rows per run
17built-in editor themes
$50perpetual release license
BUILT TO STAND ALONGSIDE THE BIG NAMES
Choose DataWorx.
Depth, control, and value.
Database development, visualization, debugging, data generation, and AI in one $50 perpetual release license.
Beyond the query editor
Table design, cross-connection structure comparison, ER diagrams, execution plans, and interactive PostgreSQL debugging sit alongside your SQL.
From raw rows to real insight
Transactional grid editing, charts, typed Excel exports, native dump/restore workflows, and realistic test-data generation belong in the same workspace.
Control the connection
Choose your provider or local Ollama model, give AI SQL/schema context, and share connections through MCP with per-connection permissions.
See where DataWorx stands out
DataWorx brings advanced database workflows together for $50. Yes = available in the named product or edition. No = absent from that edition. ? = availability unverified. Comparable capabilities can differ in implementation.
| Workflow | DataWorx | DataGrip | DBeaver Community | DBeaver Enterprise |
|---|---|---|---|---|
| Dry run for changes, plus environment tagging | ✓ Dry run changes and tag environments | ? | ? | ? |
| SQL completion & formatting | ✓ Context-aware, dialect-aware | Yes | Yes | Yes |
| Environment tagging & production guards | ✓ Mark connections as production; block UPDATE/DELETE without a WHERE; require the database name before DDL; tint the window border | ? | ? | ? |
| Database connections | Broad native SQL and NoSQL support + ODBC | Broad JDBC database support | Broad JDBC database support | Broad database support + ODBC |
| Built-in DuckDB engine | ✓ Query local CSV, Parquet, and JSON files directly; join PostgreSQL results with Snowflake results or local files | partial - no cross connection joins | partial - no cross connection joins | partial - no cross connection joins |
| MongoDB with native MQL | ✓ Shell-syntax consoles, pipeline-aware autocomplete, operator phrases and syntax help, JSON and table results | Yes | No | Yes |
| Native ODBC connections | ✓ Bundled unixODBC manager | No | No | Yes |
| Cross-connection SQL joins | ✓ Join PostgreSQL results, Snowflake results, and local files through DuckDB | ? | No | No |
| Editable data grids | ✓ All 9 connection types; stage & submit | Yes | Yes | Yes |
| Compare two result sets | ✓ Pin a result, rerun the query or run it against another connection, and highlight added, removed, and changed rows | Yes | No | Yes |
| JSON/XML value viewing | ✓ Format, color, dock, or float | Yes | Yes | Yes |
| Screen-share mode | ✓ Mask likely personal-data columns, including emails, phones, and SSNs, in grids and exports for demos and pairing | ? | ? | ? |
| Native Excel (.xlsx) export | ✓ Typed cells, filters & frozen header | Yes | ? | Yes |
| Quick Look for .sqlite / .db files | ✓ Press Space in Finder to preview tables and their first rows | ? | ? | ? |
| Visual table design | ✓ Columns, keys, indexes & CHECKs | Yes | Yes | Yes |
| Migrations from the table designer | ✓ Save generated ALTER statements as numbered migration files for Flyway, dbmate, or sqitch, and commit through Git integration | ? | ? | ? |
| ER diagrams | ✓ Auto-layout, cross-schema links, SVG/PNG | Yes | Yes | Yes |
| Schema-aware test-data generator | ✓ Up to 100,000 rows per run | ? | No | Yes |
| Execution plans | ✓ Plan trees, cost highlights & Analyze | Yes | Yes | Yes |
| Plan diff | ✓ Keep EXPLAIN plans with saved queries; compare over time or before and after an index change to see which steps worsened and by how much | ? | ? | ? |
| PostgreSQL routine debugging | ✓ Breakpoints, stepping, variables & stack | No | ? | Yes |
| Top-queries view | ✓ Read PostgreSQL pg_stat_statements, SQL Server Query Store, or MySQL performance_schema; sort by total time and explain or ask AI about any row in one click | ? | ? | ? |
| Query-result charts | ✓ Bar, line, scatter & pie | Yes | No | Yes |
| Watch mode | ✓ Rerun a query every N seconds, highlight changed cells, and optionally send a notification when a condition is met | ? | ? | ? |
| CSV/TSV import & rich exports | ✓ Mapping, validation & typed output | Yes | Yes | Yes |
| SQL notebooks | ✓ SQL, Markdown, and chart cells in one file; save as .sql/Markdown in your Git-backed query folder | ? | ? | ? |
| AI-assisted database work | ✓ Multiple providers, SQL/schema tools | Yes | Yes | Yes |
| PostgreSQL index advisor | ✓ Use HypoPG to test a hypothetical index and preview plan cost before creating it; pair with “AI, why is this slow?” | ? | ? | ? |
| External AI access through MCP | ✓ Built-in stdio server; explicit opt-in | Yes | ? | ? |
| Native desktop implementation | ✓ Built in Rust for native speed and efficiency | No | No | No |
| Native Mac links, Spotlight & Shortcuts | ✓ dataworx:// links with Spotlight and Shortcuts | ? | ? | ? |
| Spotlight and Shortcuts | ✓ Saved queries searchable from Spotlight; a Shortcuts action runs a saved query and returns rows | ? | ? | ? |
| macOS notifications | ✓ Get notified when a long query finishes or a watched query’s result changes | ? | ? | ? |
| Menu-bar item | ✓ See connection health and running queries, and open a recent query | ? | ? | ? |
| Free offering | ✓ Community edition | Non-commercial use | Community edition | Trial offerings |
| Paid release license | $50 USD · perpetual for the release | $109 USD / first year for individuals 2.18× DataWorx’s one-time price | Free Community product | $255 USD / year 5.1× DataWorx’s one-time price |
Comparison sources and scope
Additional checks: DataGrip result comparisons; DBeaver query-result comparison and edition scope; DBeaver cross-database joins; DataGrip runtime; DBeaver runtime. Native desktop implementation means a native compiled application rather than a Java runtime application. DuckDB JDBC connections are documented by both competitors; their bundled engine and combined file/result workflow remain unverified here. DBeaver cross-database joins are documented for Ultimate and Team Edition, outside the Enterprise column. Automatic refresh is documented by both products; changed-cell highlighting and conditional watch notifications remain unverified. An undocumented feature stays ? rather than being assumed absent.
Checked October 7, 2026 against official product documentation. The DataWorx column describes the product inventory. PostgreSQL debugging uses the server debugger extension; editable results use single-table primary-key data. The comparison uses DataGrip, DBeaver Community, and DBeaver Enterprise. No means the capability is absent from the named product or edition; ? means it is unverified. MCP availability is assessed for the current release, separately from documentation for upcoming releases.
- DataGrip: product & licensing, grids, values & charts, debugging, diagrams, import, export, MCP server.
- DBeaver: Community, edition matrix, charts, mock data, ODBC, export, MCP server.
Where DataWorx fits across the market
VS. DATAGRIP
IDE depth, your own workspace.
DataGrip offers a database IDE with JetBrains tooling. DataWorx brings completion, inspections, plans, diagrams, charts, and MCP together with PostgreSQL routine debugging and a $50 perpetual release license.
Official product overview ↗VS. DBEAVER
Advanced workflows, together.
DataWorx brings query-result charts, native ODBC, and schema-aware test-data generation into its product toolkit. DBeaver Community does not include these workflows. Add PostgreSQL debugging and AI/MCP, and DataWorx offers substantial depth at $50.
Official edition comparison ↗VS. TABLEPLUS
Desktop focus with a broad toolkit.
TablePlus emphasizes native database editing and SQL workflows. DataWorx pairs a compiled Rust desktop app with schema design, ER diagrams, plans, test-data generation, charts, and integrated AI/MCP.
Official product overview ↗VS. PGADMIN
PostgreSQL depth. More databases.
pgAdmin focuses on PostgreSQL administration, including plans, ERD, and debugging. DataWorx combines PostgreSQL development workflows with MySQL, MariaDB, SQLite, SQL Server, Oracle, Snowflake, and ODBC in the same app.
Official feature overview ↗VS. NAVICAT PREMIUM
A substantial workbench at $50.
Navicat Premium covers multi-database development, modeling, AI, and data generation. DataWorx offers its own integrated combination of schema tools, diagrams, charts, test data, AI/MCP, and a $50 perpetual release license.
Official product overview ↗These comparisons describe product positioning and documented functionality. Explore the full catalogue above to see the depth of the DataWorx workflow.
SIMPLE BY DESIGN
Find your edition.
Make it yours.
Start with Community, or buy a perpetual license for the release. Your workspace, your choice.
FOR THE COMMUNITY
Community
Freecommunity edition
A welcoming way to get started with DataWorx and put your data to work.
- Free Community edition
- The DataWorx desktop experience
- A place to start your database workflow
PAY ONCE. KEEP THE RELEASE.
FOR YOUR EVERYDAY WORK
Release license
$50USD · one-time payment
A straightforward perpetual license for the DataWorx release you purchase.
More workbench. Less spending. DataWorx is $50 once. DataGrip’s individual commercial plan starts at $109/year — $59 more in the first year. DBeaver Enterprise is $255/year — $205 more per year than DataWorx’s one-time price.
- Perpetual use of the purchased release
- One-time $50 license price
- No recurring license payment for that release
Prices checked October 7, 2026, in USD before tax: DataGrip individual commercial pricing and DBeaver Enterprise annual pricing. DataGrip renewal discounts apply, and annual subscriptions include a perpetual fallback license. DataGrip also offers free non-commercial use; DBeaver Community is free.
Perpetual licensing applies to the purchased release. AI provider services are configured separately with your own account or local model.
A FEW GOOD QUESTIONS
Good to know.
What does a perpetual release license mean?
You pay $50 once and can continue using the release you purchase. Future release offers and upgrade terms are announced separately.
Which databases can I connect to?
PostgreSQL, MySQL, MariaDB, Microsoft SQL Server, Oracle, SQLite, DuckDB, Snowflake, MongoDB, and generic ODBC connections.
Do I have to use AI?
No. AI is available, but DataWorx will never force it on you. Use the SQL editor, data grids, schema tools, charts, and other database workflows independently. Enable an AI assistant only when you want one.
Can I use my own AI provider?
Yes. DataWorx supports OpenAI and compatible endpoints, Anthropic, Gemini, and Ollama. You configure your own provider credentials or local model.
Can I bring my existing SQL queries?
Yes. Store saved queries as SQL files, organize them with tags and descriptions, and use a Git-backed query folder to keep your work together.
How does DataWorx fit into a macOS workflow?
DataWorx includes macOS native menus, standard shortcuts, system keychain integration, and light, dark, or system appearance.
MAKE ROOM FOR YOUR NEXT IDEA