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Supercharge your Microsoft SQL Server workflows with Deepnote

By Lukas Frei

Updated on August 11, 2022

Deepnote’s Microsoft SQL Server integration helps data teams query, extract, analyze, and model data from the comfort of a notebook environment.

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Microsoft SQL Server doesn’t really require a lengthy introduction. Thirty-three years after the release of version 1.0, it remains one of the most popular database management systems across the globe.

Deepnote set out to build an integration that enables users to streamline Microsoft SQL Server workflows using SQL and Python together. And that integration is now here!

With Deepnote’s Microsoft SQL Server integration, data teams can efficiently query, extract, analyze, and model data stored in Microsoft SQL Server databases — all within the comfort of their notebooks.

In this article, we'll walk through what makes this experience so unique and show you how to leverage Deepnote's native Microsoft SQL Server integration to supercharge your SQL workflows.

👉 If you’re eager to jump right in, sign up for Deepnote and take our Microsoft SQL Server integration for a spin!

One integration, three data sources

With Deepnote’s Microsoft SQL Server integration, you can actually do more than just connect to a Microsoft SQL Server database. Thanks to their similar underlying connection properties, you can also connect to Azure SQL or Synapse SQL. If you’d like to take a peek at what’s needed to connect to any of these three, head on over to our documentation, where we’ve compiled a list of all the resources you’ll need.

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Query with SQL and analyze with Python in one place

Deepnote provides first-class support for SQL. This means you get to query your Microsoft SQL Server (or Azure SQL or Synapse SQL) database right from your notebook. Transitions between Python and SQL are seamless, as there’s no need for a Python connector. With Deepnote, you get all the bells and whistles of a SQL editor right in your notebook, including formatting, autocomplete, linting, and even a schema explorer.

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Using SQL blocks, you can write queries, save the results as pandas DataFrames, and visualize them all in one go. This allows you to build out more complex queries with conditionality, "for" loops, and more. If you’d like to venture beyond SQL, you can also use the automatically stored pandas DataFrame and perform some advanced analytics.

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Explore SQL Server data with no code and a dash of magic

In Deepnote, a great deal of the exploratory work happens without writing any code. This allows for rapid exploration and prototyping on top of your SQL Server data.

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With Deepnote's built-in DataFrame viewer, you can easily examine a data set for missing values, the most common categorical values, distributions of numeric columns, and more. Built-in filters and sorting make it easy to gain a deeper understanding of the data and relationships that might impact a future model's predictions.

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You can also use no-code charts to examine your target variable as a function of other features without having to write any additional code. You can seamlessly switch from code to visualizations and go right back into querying as needed.

Share your insights

If you want to turn your queries, code, and charts into impactful data products, Deepnote has you covered.

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While you can easily share your notebook with one click, you can go a step further and make your work more accessible and interactive for anyone. The goal is to turn your notebook into a simple-yet-powerful data app. You can do so by parameterizing your notebook to introduce interactive user inputs and scheduling the notebook so that you’re always pulling in fresh data from SQL Server. Once that’s done, all you need to do is just hit publish.

Keep your SQL Server data secure

With Deepnote’s native integration, the connection process is secure and allows you to query data easily without exposing confidential information. You can configure the connection at the workspace level and decide whether to make it available to all members and shared projects or just a specific project. As soon as the connection is set up, your SQL Server data is safeguarded and you don’t have to worry about reconfiguring things.

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👉 Connect to your own Microsoft SQL Server instance using the Integrations menu within your Deepnote workspace.


In our workflow, we used a Deepnote notebook as a control panel for our Microsoft SQL Server instance. Using Deepnote’s Microsoft SQL Server integration, you can now benefit from Deepnote’s built-in magic as well as Python’s powerful ecosystem without compromising on your SQL editing experience.

Get Started with Microsoft SQL Server & Deepnote

Create a free Deepnote account or head over to our docs to start using Microsoft SQL Server and Deepnote together.

Lukas Frei

Product Management Associate @ Deepnote

Follow Lukas on LinkedIn and GitHub


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