Microsoft SQL Server to Tableau

This page provides you with instructions on how to extract data from Microsoft SQL Server and analyze it in Tableau. (If the mechanics of extracting data from Microsoft SQL Server seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Microsoft SQL Server?

Microsoft SQL Server is a relational database management system that supports applications on a single machine, on a local area network, or across the web. SQL Server supports Microsoft's .NET framework out of the box, and integrates nicely into the Microsoft ecosystem.

What is Tableau?

Tableau is one of the world's most popular analysis platforms. The software helps companies model, explore, and visualize their data. It also offers cloud capabilities that allow analyses to be shared via the web or company intranets, and its offerings are available as both installed software and as a SaaS platform. Tableau is widely known for its robust and flexible visualization capabilities, which include dozens of specialized chart types.

In addition to its business software, Tableau also offers a free product called Tableau Public for analyzing open data sets. If you're new to Tableau, this offering is a great way to experience Tableau's capabilities at no cost and share your work publicly.

Getting data out of SQL Server

The most common way most folks who work with databases get their data is by using queries for extraction. With SELECT statements you can filter, sort, and limit the data you want to retrieve. If you need to export data in bulk, you can use Microsoft SQL Server Management Studio, which enables you to export entire tables and databases in formats like text, CSV, or SQL queries that can restore the database if run.

Loading data into Tableau

Analyzing data in Tableau requires putting it into a format that Tableau can read. Depending on the data source, you may have options for achieving this goal, but the best practice among most businesses is to build a data warehouse that contains the data, and then connect that data warehouse to Tableau.

Tableau provides an easy-to-use Connect menu that allows you to connect data from flat files, direct data sources, and data warehouses. In most cases, connecting these sources is simply a matter of creating and providing credentials to the relevant services.

Once the data is connected, Tableau offers an option for locally caching your data to speed up queries. This can make a big difference when working with slower database platforms or flat files, but is typically not necessary when using a scalable data warehouse platform. Tableau's flexibility and speed in these areas are among its major differentiators in the industry.

Analyzing data in Tableau

Tableau's report-building interface may seem intimidating at first, but it's one of the most powerful and intuitive analytics UIs on the market. Once you understand its workflow, it offers fast and nearly limitless options for building reports and dashboards.

If you're familiar with Pivot Tables in Excel, the Tableau report building experience may feel somewhat familiar. The process involves selecting the rows and columns desired in the resulting data set, along with the aggregate functions used to populate the data cells. Users can also specify filters to be applied to the data and choose a visualization type to use for the report.

You can learn how to build a report from scratch for free (although a sign-in is required) from the Tableau documentation.

Keeping SQL Server data up to date

All set! You've written a script to move data from SQL Server into your data warehouse. But data freshness is one of the most important aspects of any analysis – what happens when you have new data that you need to add?

You could load the entire SQL Server database again. Doing this is almost guaranteed to be slow and painful, and cause all kinds of latency.

A better approach is to build your script to recognize new and updated records in the source database. Using an auto-incrementing field as a key is a great way to accomplish this. The key functions something like a bookmark, so your script can resume where it left off. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in SQL Server.

From Microsoft SQL Server to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Microsoft SQL Server data in Tableau is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Microsoft SQL Server to Redshift, Microsoft SQL Server to BigQuery, and Microsoft SQL Server to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your Microsoft SQL Server data via the API, structuring it in a way that is optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Tableau.