7 Best BigQuery GUI Tools in 2026
BigQuery has no servers to connect to in the usual sense. You work in a Google Cloud project, you pay per byte scanned or per reserved slot, and the main GUI is BigQuery Studio in the Google Cloud console. That changes what a good BigQuery GUI looks like. The question is less "can it connect" and more "does it show me what a query will cost before I run it, and can the right people see the data without a Cloud IAM role".
Below are seven BigQuery GUI tools, grouped by who they serve, with the point where each one stops.
What a BigQuery GUI needs to do
- Authenticate with Google accounts or service accounts, across multiple projects.
- Browse datasets, tables, views and partitions, including table size and partitioning columns.
- Show the estimated bytes a query will process before you run it.
- Run SQL with autocomplete and show the execution graph.
- Export results to Sheets, CSV or Cloud Storage without downloading everything.
What SQL clients do not do is give a non-technical person a narrow, safe view of the data. Each user needs a Google identity with IAM roles on the project or dataset. The first tool here works differently.
The seven BigQuery GUI tools at a glance
| Tool | Type | Who it is for | Cost |
|---|---|---|---|
| Jet Admin | Web app builder | Teams who need lookups, forms and dashboards on BigQuery data | Free plan; paid tiers |
| BigQuery Studio | Web, in the Google Cloud console | Analysts and engineers | Included; queries billed |
| Connected Sheets | Google Sheets feature | Spreadsheet users | Included with eligible Workspace plans; queries billed |
| Looker Studio | Dashboards | People building shared reports | Free; Pro tier paid |
| DBeaver | Desktop, multi-database | Engineers across several databases | Free Community; paid PRO |
| DataGrip | Desktop IDE | Developers who keep SQL in a repository | Paid; free for non-commercial use |
| Hex | Collaborative notebook | Analysts sharing SQL and Python work | Free tier; paid plans |
For teams working on BigQuery data
1. Jet Admin
Jet Admin connects to BigQuery and builds an app on it: searchable tables, record detail pages, forms and dashboards that read live from your datasets. You describe what you need and Jet generates the screens, then you refine them in a visual editor or in code. Jet queries BigQuery in place and keeps no second copy of your data.
The difference is who can use it. An account manager who needs usage for one customer, or an operations lead who needs yesterday's orders by region, signs in to the app and sees only what their role allows. You connect BigQuery once with a service account scoped to the datasets the app needs, and every screen runs a query you designed and can filter on partitioned columns, so nobody runs an accidental full scan. Granular permissions, SSO and audit logs are on the Business plan and above; unlimited users are on every plan, including Free.
Where it stops: it is not a place for ad hoc SQL, execution graphs or managing datasets. Keep BigQuery Studio for that.
Built by Google
2. BigQuery Studio
BigQuery Studio is the BigQuery workspace in the Google Cloud console. It combines a SQL editor that shows estimated bytes processed before you run a query, an execution graph, saved and shared queries, Python notebooks, data canvas views and Gemini assistance. For anyone with the right IAM roles, it covers most daily work.
Where it stops: every user needs a Google Cloud identity with project or dataset permissions, and it is built for people who write queries.
3. Connected Sheets
Connected Sheets lets people analyze BigQuery tables from inside Google Sheets, with pivot tables, charts and formulas that run against BigQuery rather than on data copied into the sheet. For spreadsheet-first teams, it is the most familiar interface.
Where it stops: it is for analysis, not for editing records, and each refresh runs a BigQuery query that is billed to the project.
4. Looker Studio
Looker Studio is Google's free dashboarding tool with a native BigQuery connector. You build reports with charts and filters and share them with viewers who do not need BigQuery access, depending on how you set data credentials.
Where it stops: it builds read-only reports. Forms, record updates and workflows are outside its scope.
Desktop clients and notebooks
5. DBeaver
DBeaver connects to BigQuery through a JDBC driver and offers its usual schema browser and SQL editor. It is useful when BigQuery is one of several databases you query from the same client.
Where it stops: it does not show bytes-processed estimates the way BigQuery Studio does, which makes it easier to run an expensive query by accident.
6. DataGrip
DataGrip supports BigQuery with schema-aware completion and refactoring, and suits developers who keep BigQuery SQL in version control, for example in dbt projects.
Where it stops: it is paid for commercial use and aimed at developers.
7. Hex
Hex is a collaborative notebook with a native BigQuery connection, mixing SQL, Python and charts. Analysts use it to explore data together and publish interactive results for stakeholders.
Where it stops: it is an analysis tool, not an operational one. People read results in Hex; they do not run processes in it.
Which BigQuery GUI to use
- People outside the data team need BigQuery data in a safe, narrow view: Jet Admin.
- You write SQL against BigQuery every day: BigQuery Studio.
- Your team lives in spreadsheets: Connected Sheets.
- You need shared, read-only dashboards: Looker Studio.
- BigQuery is one of several databases you use: DBeaver.
- Your SQL lives in a repository: DataGrip.
- Analysts explore and present data together: Hex.
Frequently asked questions
Does BigQuery have a GUI?
Yes. BigQuery Studio in the Google Cloud console is BigQuery's web interface, with a SQL editor, notebooks and data exploration tools.
Is the BigQuery console free?
Using the console is free, but queries are billed by bytes processed under on-demand pricing, or against your reserved capacity. BigQuery also includes a monthly free query allowance.
Can I use DBeaver with BigQuery?
Yes. DBeaver connects to BigQuery through a JDBC driver using a Google account or a service account key. Check query costs in BigQuery Studio first, because DBeaver does not show a bytes-processed estimate.
How do I give non-technical users access to BigQuery data?
Avoid giving each person a Cloud IAM role. Build an app on top of BigQuery that connects with a scoped service account, runs fixed queries on partitioned columns, and shows each role only what it needs. Jet Admin is built for that.
Start with who needs the data
If everyone who needs BigQuery data writes SQL, BigQuery Studio will serve them. If they are in sales, support or operations, the answer is an app that asks the right query for them. Connect BigQuery to Jet Admin and build it on the datasets you already have.