Quickstart

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.

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). For the last step, the permission differs: can_use_notebooks for the notebook route, or can_use_dashboards for the SQL route.

Go to https://<your-databaas-domain> and sign in. The portal tour explains what you see.

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.

Find a table

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.

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.

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:

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.

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):

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.

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.