Query your databases or CSV files, chart the results, and add Python when you need it, all in one shared workspace.
Deepnote keeps your queries, results and explanations together, so your team can follow the work from the first question to the final answer. Start with a supported data source and build an analysis others can review, reuse and extend.
SQL notebooks give you room to develop a question in stages. Inspect intermediate results, record what you checked, and keep useful queries ready for the next investigation.
Use schema-aware autocomplete to find relevant tables and columns as you write. When an analysis needs several steps, query chaining lets you reference earlier query previews in subsequent SQL blocks.
Break supported SELECT queries into manageable pieces, inspect the output, and explain where a filter or definition changes the interpretation.
Describe the question in plain language—for example, “Show weekly orders for the last three months, excluding test orders.”
Deepnote Agent uses available schema and notebook context to help draft a query for your connected SQL dialect. Ask it to revise the query, explain unfamiliar logic or suggest a fix for an error.
Check the tables, joins, filters and assumptions before running the result. AI availability depends on your plan, workspace settings and connection.
For eligible connections, SQL query caching can return recent results instead of repeating a warehouse query. Set an expiration period, or run without cache when your analysis needs fresh data.
Start with SQL to select, filter and aggregate your data, then use the query results in Python for statistical analysis, forecasting or custom visualizations. Deepnote keeps SQL and Python blocks in the same notebook, so you can move between querying and further analysis while keeping the code, results and explanations together.
Deepnote is recognized as a Leader and a Mid-Market High Performer in G2’s Analytics Platforms category.
“Deepnote makes pulling and exploring data so much easier. We can quickly connect to databases and get straight into analysis, which has really improved our visibility into performance and helped speed up decision-making. It’s also incredibly flexible — we’ve been able to build models that help ensure operational quality and adapt them as our needs evolve...”
Read the full Deepnote review“Combining of SQL blocks and Python code is very seamless and easy to use. I believe the user experience overall was pretty good. It’s easy for me to get the hang of it after just an hour of using and exploration. As well as how easy it is to integrate different platforms such as Snowflake and OpenAI into my projects...”
Read the full Deepnote review“I’ve used Deepnote for everything from one-off dashboards for clients to big solo projects that involve stitching together data from multiple sources. It allows easy switching between Python and SQL in the same notebook (and maybe R? I haven’t tried that yet though!). It is also straightforward to integrate external data from Sheets or Snowflake...”
Read the full Deepnote reviewBring data from supported databases and warehouses into your analysis. Configure the integration, attach it to your project, and select it in a SQL block.
Follow the setup guide for your source’s credentials, network access and plan requirements. Each connection has its own configuration; choose the one that matches where your data lives.
PostgreSQL: Query your PostgreSQL database from the notebook.
Snowflake: Explore warehouse data and build on the results.
BigQuery: Query your datasets and continue the analysis in Deepnote.
Microsoft SQL Server: Connect SQL Server, Azure SQL or Azure Synapse.
CSV files and DataFrames: Use DataFrame SQL to explore tabular files or pandas DataFrames without first connecting a warehouse.
Explore a cloud cost analysis, which starts with a SQL query and continues into a spend breakdown, a daily refresh, and a shared dashboard.
Explore examples of AI-assisted data analysis, sports analytics and churn modeling. Follow the queries, calculations and visualizations to see how an investigation comes together.
Deepnote Agent Workspace combines notebooks, data connections,
and business knowledge so agents can work with your data’s context.
Connect through Cursor, Claude Code, Codex, and other MCP-compatible tools. Access Deepnote notebooks and data within existing permissions.
Schedule notebook workflows or trigger them through the API. Automate reports, data checks, and recurring analyses.
Keep metric definitions, assumptions, analysis steps, and business context alongside your code. Turn trusted work into reusable skills, agents, and data apps.
Find the details for your next step:
Bring your data, run a query, and keep the result and the explanation in one place. Build an analysis that your team and agents can reuse.