Amazon Aurora to BigQuery

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

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 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.

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 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.

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