MySQL to Snowflake

This page provides you with instructions on how to extract data from MySQL and load it into Snowflake. (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.

About Snowflake

Snowflake is a data warehouse solution that is entirely cloud based. It's a managed service. If you don't want to deal with hardware, software, or upkeep for a data warehouse you're going to love Snowflake. It runs on the wicked fast Amazon Web Services architecture using EC2 and S3 instances. Snowflake is designed to be flexible and easy to work with where other relational databases are not. One example of this is the query execution. Snowflake creates virtual warehouses where query processing takes place. These virtual warehouses run on separate compute clusters, so querying one of these virtual warehouses doesn't slow down the others. If you have ever had to wait for a query to complete, you know the value of speed and efficiency for query processing.

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).

Preparing data for Snowflake

Depending on the structure that you data is in, you may need to prepare it for loading. Take a look at the supported data types for Snowflake and make sure that the data you've got will map neatly to them. If you have a lot of data, you should compress it. Gzip, bzip2, Brotli, Zstandard v0.8 and deflate/raw deflate compression types are all supported.

One important thing to note here is that you don't need to define a schema in advance when loading JSON data into Snowflake. Onward to loading!

Loading data into Snowflake

There is a good reference for this step in the Data Loading Overview section of the Snowflake documentation. If there isn’t much data that you’re trying to load, then you might be able to use the data loading wizard in the Snowflake web UI. Chances are, the limitations on that tool will make it a non-starter as a reliable ETL solution. There two main steps to getting data into Snowflake:

  • Use the PUT command to stage files
  • Use the COPY INTO table command to load prepared data into the awaiting table from the prior step.

For the COPY step, you’ll have the option of copying from your local drive, or from Amazon S3. One of Snowflakes’ slick features lets you to make a virtual warehouse that will power the insertion process.

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.

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 Snowflake data warehouse.