# Quickstart (/getting-started/quickstart)

Go from signing in to reading your first table, end to end.

Sign in, find a real table in the Data Catalog, and read rows from it — first in the catalog, then
from a notebook or in SQL Lab. New to warehouses, namespaces and Iceberg tables? Start with
[core concepts](/getting-started/concepts).

<Callout title="What you need">
  A Databaas login and a grant on at least one table — if the catalog says **No
  rights yet**, ask an administrator ([data
  permissions](/catalog/data-permissions)). For the last step, the permission differs:
  `can_use_notebooks` for the notebook route, or `can_use_dashboards` for the
  SQL route.
</Callout>

## Sign in

Go to `https://<your-databaas-domain>` and sign in. The [portal tour](/getting-started/portal-tour)
explains what you see.

<Callout title="A feature you need is missing?">
  The portal shows only features you can open. If one you need isn't listed, you
  don't have its permission yet — ask an administrator. See [how access
  works](/access/how-access-works).
</Callout>

## Find a table

<Steps>

<Step>
### Browse or search

In **Data Catalog**, the left pane lists the warehouses you can read. Expand into namespaces and
tables, or filter with the **Search** field. Select a table; the breadcrumb shows its identity —
**warehouse**, **namespace**, **table name** — which the routes below need.

</Step>

<Step>
### Look before you query

The detail pane opens on **Preview**: the first 20 rows of the current snapshot, read with your own
grants — a sample, not an analysis. **Schema**, **Properties** and **Permissions** tabs sit beside it. If the
preview looks right, you have your table.

</Step>

</Steps>

## Read it

Read from a **notebook** (Python) or **SQL Lab** (SQL); both return the same rows.

### In a notebook

Requires `can_use_notebooks` permission. Open **Notebooks** (the first launch can take a minute while your server starts) and create a
Python notebook. `databaas-utils` fetches a token for _you_, so the catalog is scoped to
your grants:

```python title="Load a table with pyiceberg"
from databaas_utils.notebook import get_catalog

catalog = get_catalog()
table = catalog.load_table("<namespace>.<table>")
print(table.scan(limit=10).to_arrow().to_pandas())
```

Use the breadcrumb values; for a table outside the default warehouse, pass
`get_catalog(warehouse="<warehouse>")`. A grant error means that table isn't one of your
grants; an authentication error ("…log out of Jupyter and log back in…") means your sign-in aged out —
sign out of Notebooks and back in. To narrow a
scan and query it with DuckDB, see [querying data](/notebooks/querying-data).

### In SQL Lab

Requires `can_use_dashboards` permission. Open **Dashboards**, then **SQL → SQL Lab** in Superset's top menu. Choose the **Lakehouse** database
and pick your namespace from the schema list (namespaces appear as schemas):

```sql title="First rows, in SQL"
SELECT * FROM <namespace>.<table> LIMIT 10
```

The query runs using your grants, so a table you could not preview will not return rows here either.

<Callout type="warn">
  Feature access and data access are separate. **Use dashboards** only
  controls whether you can open Dashboards — everyone with that permission
  sees the same list of charts and datasets. Whether a chart actually returns
  rows depends on your data grants, checked on every query just like in SQL
  Lab above: a chart built on data you have no grant for errors instead
  of showing empty results. See [how access
  works](/access/how-access-works).
</Callout>