The launch of a new Google Data Studio connector isn’t typically something to write home about. But among the latest rolling updates to Google Data Studio, a beta connector to Microsoft SQL server merits interest. Google’s Microsoft SQL 2017 Server connector for its cloud-based platform makes it easier to bring data from Microsoft SQL databases into Google Data Studio.
Until now, two choices were possible for working with Google Data Studio and Microsoft database services. Analysts either had to use a Microsoft solution such as Power BI or they had to export the data in one step, then import the data into Data Studio. With the connector, users can rely on Data Studio to integrate the data into familiar tables.
Setting Up the Microsoft SQL — Google Data Studio Connection
The connection is straightforward to set up. To connect to the Microsoft database, enter your Data Studio account and navigate to the “Add data to report” menu item. Select “Microsoft SQL Server connection from the menu. You then have two choices for connection to a data source: by JDBC URL or by details for hostname/IP address. You can choose to enable SSL or not, then authenticate and connect.
According to Google on its Data Studio help page, the connector uses the V7.2 JDBC driver, and is also compatible with the 2016, 2014, 2012 and 2008 versions of Microsoft SQL.
When the data is imported, Google Data Studio maps the data type from the source database to a specific list of data types, then it sets ups the columns and rows. When querying, Data Studio will skip over columns that do not contain the supported data type and will load up to 150,000 rows from a given query.
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What the Microsoft SQL Connector Brings to an Analyst’s Workflow
The Microsoft SQL connector for Google Data Studio reflects a growing trend among data solution providers for business and software development. Integrated development environments (IDEs) and software solutions have introduced features that integrate data and programming environments more easily. R Studio, a popular IDE among data scientists who use R Programming, recently added integration upgrades so that users can run Python scripts more easily within a project file or a project notebook. These integrations benefit analysts with the potential for enhanced workflow consistency. Developers and analysts spend less time switching back and forth between languages and interface environments.
Tighter integrations make better data quality audits possible, since users can verify that programming and environments have the same setup. Google has introduced user features that support data auditing, such as conditional formatting and version control. Being in an environment with similar controls helps teams quickly eliminate individual and systematic errors. It also allows analysts to remain focused on choosing the best visualization that communicates a vital story from the data.
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Better Data Integration
The connector represents a small yet significant milestone for how far tech solution providers have come in responding to martech integration concerns. Google Data Studio has added connectors over the years, but these were developed for other Google services like BigQuery or for MySQL. This gave Google Data Studio an ecosystem that is comparable to Power BI or Tableau. The connector eliminates a step requiring dedicated usage of Microsoft services for accessing data from Microsoft servers.
Establishing an ecosystem can avoid a team using too many solutions to get work done. Instead of working with a true martech stack, a team gains a “frankenstack,” a term Mike Barry of Shutterfly uses to describe a hackneyed collection of poorly integrated tools (He explains his concerns in this post). With so many teams working remote and in blended work environments, the need to better integration becomes clear.
SQL databases are an operational staple in many companies, so I expect analyst adoption of the Google Data Studio connector will be fast. The connector will alleviate workflow steps for teams, especially for small groups seeking an efficiency gain in transferring data between sources.
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Pierre DeBois is the founder of Zimana, a small business digital analytics consultancy. He reviews data from web analytics and social media dashboard solutions, then provides recommendations and web development action that improves marketing strategy and business profitability.