MySQL to BigQuery

This page provides you with instructions on how to extract data from MySQL and load it into Google BigQuery. (If this manual process sounds onerous, 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 is the backbone of countless websites and applications, and chances are you interact with MySQL-powered technology every day.  However, MySQL is largely used as a transactional or operational database, and is not nearly as optimized for analytics as Google BigQuery.

What is Google BigQuery?

Google BigQuery is a data warehouse that delivers super-fast results from SQL queries, which it accomplishes using a powerful engine dubbed Dremel. With BigQuery, there's no spinning up (and down) clusters of machines as you work with your data. With all of that said, it's clear why some claim that BigQuery prioritizes querying over administration. It's super fast, and that's the reason why most folks use it.

Getting data out of MySQL

There are several methods for extracting data from MySQL, and the one you use will probably be dependent upon your needs (and skill set).

The most common way is simply writing queries. SELECT queries allow you to pull exactly the data you want by specifying filters, ordering, and limiting results. If you have a specific subset of data in mind or are looking to continuously monitor a subset of a specific table, SELECT queries may be a good fit.

If you're just looking to export data in bulk, however, there may be an easier way. 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 (i.e. delimited text, CSV, or SQL queries that would restore the database if run).

Loading data into Google BigQuery

Google Cloud Platform offers a helpful guide for loading data into BigQuery. You can use the bq command-line tool to upload the files to your awaiting datasets, adding the correct schema and data type information along the way. The bq load command is your friend here. You can find the syntax in the bq command-line tool quickstart guide. Iterate through this process as many times as it takes to load all of your tables into BigQuery.

Keeping MySQL data up to date

Ok great! The script you have now should satisfy all your data needs for MySQL... right? Not yet. There is one big aspect left to consider: how do we continuously load data that is new or updated? It's not a good idea to just replicate all of your data each time you have updated records. That process is going to be painfully slow, and if latency is important to you then it's definitely not a viable option.

You'll need to identify some key fields that your script can use as primary keys to bookmark its progression through the data. This way, you're ETL script can pick up where it left off and look for updated data. The fields that work best for this are auto-incrementing (i.e. updated_at or created_at). 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.

Other data warehouse options

BigQuery is really great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Postgres or Redshift, which are two RDBMSes that use similar SQL syntax. If you're interested in seeing the relevant steps for loading this data into Postgres or Redshift, check out To Redshift and To Postgres.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your MySQL data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Google BigQuery data warehouse.