platform / notebooks
Python on your data, ready in seconds.
Notebooks give you a private JupyterLab environment inside the platform, already connected to your warehouse. Open one and start analysing, with no keys or setup.
# the warehouse is already connected %%bigquery df select cohort_month, avg(retained) as retention from `{DATASET}.cohort_retention` group by 1 import plotly.express as px px.line(df, x="cohort_month", y="retention")
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see it
From a query to a chart in two cells
The warehouse is already connected. Query with SQL, analyse in Python and save the notebook to your repository.
%%bigquery df select week, sum(revenue) as revenue from `{DATASET}.revenue_daily` group by 1 order by 1
import plotly.express as px px.line(df, x="week", y="revenue")
// illustrative
what you get
Notebooks, in detail
JupyterLab, built in
Each project gets its own private notebook environment, started from the Notebooks page.
Connected to your warehouse
A preconfigured BigQuery client, a dataset variable for your dbt models and the %%bigquery magic. No key file needed.
The libraries you expect
pandas, NumPy, SciPy, scikit-learn, matplotlib, seaborn and Plotly come preinstalled.
Versioned with your code
Notebooks are saved in the notebooks folder of your repository, so you can commit them from Build.
Safe by default
Queries are capped at 100 GB billed by default, access is limited to your project and internet access is limited to public HTTPS.
Shareable results
Viewers cannot run code, but can open the latest saved output of a notebook in read-only mode.
how it works
From setup to answers
01 /
Start
An editor or admin starts the environment from the Notebooks page. The first start takes a little while.
02 /
Query
Use the preconfigured client or the %%bigquery magic to pull data into pandas.
03 /
Analyse
Use Python libraries for statistics, modelling and charts.
04 /
Save and share
Save to the notebooks folder, commit from Build and let viewers open the latest output.
faq
Frequently asked questions
Which language do notebooks use?
Python, in JupyterLab.
Do I need to set up credentials?
No. The environment is already authorised for your project, and a BigQuery client is preconfigured.
Which libraries are included?
pandas, NumPy, SciPy, scikit-learn, matplotlib, seaborn and Plotly are preinstalled.
Is there a cost limit?
Each BigQuery query is capped at 100 GB billed by default. A single query can use a different cap by passing its own job configuration.
Where are notebooks stored?
In the notebooks folder of your project repository, so they can be committed from Build like other code.
Can everyone run notebooks?
Editors and admins can start and run them. Viewers can open the most recently saved output in read-only mode.
How long does the environment stay on?
It stops automatically after 60 minutes of inactivity and can be restarted. Files in the notebooks folder are kept.
Tell us what you need. We'll scope it.
Pricing is tailored to your sources, volume and the parts of the platform you use. Request a demo or book a call with the team.