This page provides you with instructions on how to extract data from MySQL and analyze it in Power BI. (If the mechanics of extracting data from MySQL 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 MySQL?
MySQL is the world's most popular open source relational database management system (RDBMS). It's the data store for countless websites and applications; chances are you interact with MySQL-powered technology every day. MySQL is largely used as a transactional or operational database, and not as much for analytics.
What is Power BI?
Power BI is Microsoft’s business intelligence offering. It's a powerful platform that includes capabilities for data modeling, visualization, dashboarding, and collaboration. Many enterprises that use Microsoft's other products can get easy access to Power BI and choose it for its convenience, security, and power.
With high-value use cases across analysts, IT, business users, and developers, Power BI offers a comprehensive set of functionality that has consistently landed Microsoft in Gartner's "Leaders" quadrant for Business Intelligence.
Getting data out of MySQL
MySQL provides several methods for extracting data; the one you use may depend upon your needs and skill set.
The most common way to get data out of any database is simply to write queries. SELECT queries allow you to pull the data you want. You can specify filters and ordering and limit results.
If you're looking to export data in bulk, there's an easier alternative. Most MySQL installs include a handy command-line tool called mysqldump that allows you to export entire tables and databases in a format you specify, including delimited text, CSV, or an SQL query that would restore the database if run.
Loading data into Power BI
You can analyze any data in Power BI, as long as that data exists in a data warehouse that's connected to your Power BI account. The most common data warehouses include Amazon Redshift, Google BigQuery, and Snowflake. Microsoft also has its own data warehousing platform called Azure SQL Data Warehouse.
Connecting these data warehouses to Power BI is relatively simple. The Get Data menu in the Power BI interface allows you to import data from a number of sources, including static files and data warehouses. You'll find each of the warehouses mentioned above among the options in the Database list. The Power BI documentation provides more details on each.
Analyzing data in Power BI
In Power BI, each table in the data warehouse you connect is known as a dataset, and the analyses conducted on these datasets are known as reports. To create a report, use Power BI’s report editor, a visual interface for building and editing reports.
The report editor guides you through several selections in the course of building a report: the visualization type, fields being used in the report, filters being applied, any formatting you wish to apply, and additional analytics you may wish to layer onto your report, such as trendlines or averages. You can explore all of the features related to analyzing and tracking data in the Power BI documentation.
Once you've created a report, Power BI lets you share it with report "consumers" in your organization.
Keeping MySQL data up to date
The script you have now should satisfy all your data needs for MySQL – right? Not yet. How do you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow; if latency is important to you, it's not a viable option.
Instead, you can identify some key fields that your script can use to bookmark its progression through the data, and pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. 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 MySQL.
From MySQL to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing MySQL data in Power BI 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 MySQL to Redshift, MySQL to BigQuery, MySQL to Azure SQL Data Warehouse, MySQL to PostgreSQL, MySQL to Panoply, and MySQL to Snowflake.
Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate MySQL with Power BI. With just a few clicks, Stitch starts extracting your MySQL data via the API, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Power BI.