This page provides you with instructions on how to extract data from MySQL and analyze it in Google Data Studio. (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 Google Data Studio?
Google Data Studio is a simple dashboard and reporting tool. It's free and easy to use, but it lacks the sophisticated features of higher-end reporting software. Many of the connectors it supports are for Google products, but third parties have written partner connectors to a wide variety of data sources. Its drag-and-drop report editor lets users create about 15 types of charts.
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 Google Data Studio
Google Data Studio uses what it calls "connectors" to gain access to data. Data Studio comes bundled with 17 connectors, mostly to pull in data from other Google products. It also supports connectors to MySQL and PostgreSQL databases, and offers 200 connectors to other data sources built and supported by partners.
Using data in Google Data Studio
Google Data Studio provides a graphical canvas onto which users drag and drop datasets. Users can set dimensions and metrics, specify sorting and filtering, and tailor the way reports and charts are displayed.
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 Google Data Studio 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 Synapse Analytics, 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 Google Data Studio. With just a few clicks, Stitch starts extracting your MySQL data, 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 Google Data Studio.