Amazon Aurora to Snowflake

This page provides you with instructions on how to extract data from Amazon Aurora 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 Amazon Aurora?

Aurora is a MySQL-compatible relational database. It is used by those looking for better performance than a traditional MySQL database at cost-effective price points. As a result, Aurora is largely used as a transactional or operational database and is by no means optimized for analytics.

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 Amazon Aurora

There are several methods for extracting data from Amazon Aurora, 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. A handy command-line tool called mysqldump 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 Amazon Aurora data

For every table in your Amazon Aurora database, you're going to need a corresponding table in your destination database. There are lots of important parts of this process; I'll highlight two of them here. First, make sure you have pinpointed all of the fields that will be inserted into your destination. You don't want to be heading back to edit the destination tables during the insertion step. Second, determine the datatypes for each object to make sure they are mapped properly when they get inserted into the new table. The more setup you do on this step, the more headache you'll avoid later.

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 Amazon Aurora data up to date

So, now what? You’ve built a script that pulls data from Amazon Aurora and loads it into your warehouse, but what happens tomorrow when you have new and updated records in your Amazon Aurora database?

Depending on how you’ve built your script, you may be forced to load your entire database again. This might be slow and painful, or even have performance implications on your Amazon Aurora instance.

The key is to build your script in such a way that it can also identify incremental updates to your data. If your Amazon Aurora tables have fields like modified_at or auto-incrementing primary keys, you can build a script that can quickly identify records that are new or changed since your last update (or since the newest record you’ve copied into the destination). You can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

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 Amazon Aurora data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Snowflake data warehouse.