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– by Lukas on August 11, 2022

Supercharge your Microsoft SQL Server workflows with Deepnote and Python

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 notebook environment.

Microsoft SQL Server doesn’t really require a lengthy introduction—33 years after the release of version 1.0, it remains one of the most popular database management systems across the globe. To truly integrate Microsoft SQL Server with modern data analytics workflows using SQL and Python interchangeably, we set out to build an integration that enables users to streamline Microsoft SQL Server workflows.

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 notebook environment.

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 down the deep end, sign up for Deepnote here and take our Microsoft SQL Server integration for a spin!

1. 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.

Deepnote integration connectivity.png

2. 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 database (or Azure SQL, or Synapse SQL) 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.

SQL Server queries.png

Using Microsoft SQL Server SQL blocks, you can write queries, save the results into 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.

SQL Server and Python tools.png

3. Explore SQL Server data with no code and a dash of ✨

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

SQL Server DataFrame exploration.png

With Deepnote's built-in DataFrame viewer, we can easily examine the dataset 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 our future model's predictions.

SQL Server viz.png

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

4. Share your insights

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

SQL Server present.png

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, even without code. 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 just hit publish.

5. Keep your Microsoft SQL Server data secure

With Deepnote’s native integration, the connection process is secure and allows us to query data easily without exposing confidential data. We 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 secure, and we don’t have to worry about re-configuring things again.

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

Conclusion

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
To start using Microsoft SQL Server and Deepnote together, create a free Deepnote account here, or head over to our docs for more.

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Lukas Frei

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