What Can You Do With Deepnote?

Deepnote is an open data workspace where you can explore data with Python and SQL, build data apps and dashboards, and collaborate with a team in one place. It fits teams that want notebooks, dashboards, and shared data work under one roof rather than stitching together separate tools. The site describes it as "the open data workspace built for humans agents," with 600,000+ data professionals using it.

Explore and analyze data with Python and SQL

The core of Deepnote is the notebook: you write Python and SQL in cells, run them, and see results inline. The homepage example shows a SQL query joining an A/B test assignment table with a churn events table, then computing churn rate by variant (control 4.2% vs treatment 7.8%). You can add follow-up cells to explain drivers, and the example lists top drivers like tutorial_skipped, no_team_invite, and first_session < 2m.

What this means in practice:

Query data sources directly — Deepnote lists "100s of integrations," so you connect a data source and query it without exporting files first.
Mix SQL and Python — run SQL for aggregation, then use Python for modeling or visualization in the same notebook.
Iterate in cells — each cell is an input, an action, and a visible result, so you can adjust a query and re-run just that cell.

Build data apps and dashboards

Beyond static notebooks, Deepnote supports "data apps & dashboards" so you can turn an analysis into something shareable. The homepage shows a PLG dashboard with live metrics (weekly signups 128k +12%, activated 41% +6%, at risk 742 -18%) and a pipeline-by-segment breakdown, marked "published · shared with revenue team."

Use this when:

You need a live app that updates rather than a one-off chart.
You want to share insights with people who won't open a notebook.
You're tracking metrics over time and want alerts when they move — Deepnote lists "Alerts: Monitor your metrics."

Collaborate with your whole team

Collaboration is a first-class feature, not an add-on. The site lists:

Code reviews — "Catch bugs early."
History & versioning — "Go back in time."
Real-time teamwork — "Collaborate with the whole team."

For a team, this replaces the usual pattern of emailing notebook files or pasting screenshots into chat. Reviewers can comment on code, and you can revert to an earlier version if something breaks.

Run data engineering and ML workflows

Deepnote covers more than analysis:

Data engineering — ETL/ELT pipelines, data transformation, data catalog, documenting data assets, Spark and Snowpark for high-performance computing.
Machine learning — model training, model serving from a notebook, model monitoring, and GPUs for hardware-heavy work.
Semantic layer — modules, LookML, and dbt, so metrics definitions can stay consistent across work.

If your work spans "clean the data, train a model, then serve it," Deepnote aims to keep that in one workspace instead of moving between a pipeline tool, a notebook, and a serving stack.

Use AI assistance and autonomous agents

Deepnote's current positioning highlights AI and agents. The homepage shows a "Deepnote AI insight" that flags: "Treatment users skipped onboarding 2.4x more often. Segment by first-session length before shipping." There's also a fraud-detection agent example that plans next steps and generates SQL blocks.

Treat these as accelerators, not replacements: the AI suggests a direction or writes a starting query, and you still verify the logic and the numbers before acting on them.

Security and compliance

For teams with requirements, Deepnote lists "Security & auditing" and names SOC2 and HIPAA among its compliance signals. Check the specific controls your organization needs against Deepnote's own documentation before committing, since the homepage only names these at a high level.

Who it's for

The site groups customers by industry — fintech & finance, biotechnology, gaming, enterprise, startups, and research — and notes use in education. If your team writes Python or SQL, shares results with non-coders, and wants review and versioning built in, Deepnote is aimed at you. If you only need a personal scratch notebook, a lighter tool may be enough.

To decide, start from your most common task: if it's "explore data, then share a dashboard with the team," Deepnote's notebook-plus-apps combination is the direct fit.