> For the complete documentation index, see [llms.txt](https://v2.dataos.info/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://v2.dataos.info/build/stage-1-discover/explore-data.md).
# Explore data
Once the metadata looks promising, you need to validate the actual data. The Workbench is where you do that. Use it before you commit a dataset to analysis, reporting, an application, or a data product. A few minutes here saves rework later.
***
## What's in Workbench
Three areas, in the order you'll use them:
| Area | What it's for |
| --------------- | ------------------------------------------------------------------- |
| Workbench home | Create a new bench, open a saved one, or pick up an unsaved draft. |
| New bench setup | Choose the data source and the Secret used to connect to it. |
| Query workspace | Write SQL, run queries, browse schemas and tables, inspect results. |
***
## Exploration flow
{% stepper %}
{% step %}
### Open Workbench
Open **Workbench** from the left navigation.
The home page shows your saved benches. From here you can:
* Create a new bench.
* Reopen unsaved benches.
* Return to a previous one.
Click **New bench** to start.
{% endstep %}
{% step %}
### Pick a data source
The selection page lists every data source you have access to, with owner, source type, and creation time.
Search by keyword, or filter by type, owner, or creation date. Pick the source and continue.
{% endstep %}
{% step %}
### Pick a Secret
After choosing a data source, select the Secret that Workbench should use to connect to it.
In DataOS, a connection is made of two resources:
* A Data Source (Depot) holds the connection details, where the source is and how to reach it.
* A Secret holds an access identity, the credentials, token, or role used to connect.
Workbench needs both: the Depot tells it where to connect, and the Secret tells it which credential to connect with. A single Depot can have several Secrets attached, each representing a different credential or permission context (for example, full access, masked or read-only access, or limited access).
{% hint style="info" %}
What a credential can actually allow( read, write, see masked data, or nothing) is set by the source system's own roles and policies.
{% endhint %}
#### Common Secret naming conventions
| Secret | Use it when |
| ------- | --------------------------------------------------------- |
| `query` | You only need to query and explore. |
| `scan` | You need metadata scanning or schema discovery. |
| `rw` | You need read-write access and have permission to use it. |
As more than one credential can be valid for the same Depot, Workbench cannot safely pick one for you; so choose the secret that matches what you intend to do.
{% hint style="info" %}
When you share a bench, the selected secret sets the permission context that others inherit. For example, the bench owner can choose a masked, read-only secret so others can explore the data without seeing sensitive fields or making unintended changes.
{% endhint %}
{% endstep %}
{% step %}
### Open the bench
With source and Secret selected, the bench opens. A bench is your working space for SQL. You can keep several open at once; each one is a tab at the top.
From the bench header, you can:
* Rename the bench.
* Save it.
* Open more benches.
* Run all queries.
* Copy, expand, and use other workspace actions.
{% endstep %}
{% step %}
### Browse schemas and tables
The left panel shows the source's schemas, tables, and columns. Use it to learn the layout before you write SQL.
Expand a schema to see its tables. Select or pin a table to see its columns.
{% endstep %}
{% step %}
### Inspect columns
Selecting a table shows columns and types in the left panel. Look for:
* Identifier fields.
* Categorical fields.
* Possible metric or measure fields.
* Fields that need filtering, transformation, or validation.
Decide what's relevant before writing the query.
{% endstep %}
{% step %}
### Write a query
Type your SQL into the editor.
{% endstep %}
{% step %}
### Run it
Click the run icon on a cell, or **Run all** for every cell in the bench. The execution time and returned rows tell you whether the query does what you expected.
{% endstep %}
{% step %}
### Refine
Once you've seen the first result set, iterate. You can run the next query against the source, or run it locally against the previous cell's result using a cell reference.
Cell references skip the round trip to the source, so they're faster and cheaper.
Add a cell and click the cell-reference icon to enable it.
{% hint style="info" %}
Cell references run locally. Performance scales with dataset size and your machine's memory.
{% endhint %}
{% endstep %}
{% step %}
### Work with multiple benches
Each bench is a tab. Open several when you want to:
* Compare different sources.
* Compare different tables.
* Keep drafts separate from validated queries.
* Try a few approaches without losing earlier work.
Unsaved benches stay around until you close them or save them.
{% endstep %}
{% step %}
### Save the ones worth keeping
Save a bench when:
* You want to continue exploration later.
* You're preparing a query for reporting, APIs, or downstream data product work.
* You need to share or revisit the exploration.
{% endstep %}
{% endstepper %}
## What's next
Pick the next move based on what you found:
| Outcome | Next action |
| ------------------------ | ----------------------------------------------------------------------------- |
| Keep exploring | Inspect more tables, columns, or values. |
| Use the data in analysis | Use the dataset if it is relevant and the query output is valid. |
| Build a data product | Use the dataset if it supports a reusable business use case. |
| Try another source | Choose another source if the selected one does not contain the data you need. |
| Source the data first | Bring the data first if it is not yet available with the underlying engine. |
---
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