This page provides you with instructions on how to extract data from Amazon Aurora and load it into Redshift. (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?
Amazon Aurora is a MySQL-compatible relational database employed by organizations that are looking for better performance than they can get from MySQL at cost-effective price points. Aurora is best used as a transactional or operational database and not for analytics.
Getting data out of Amazon Aurora
Aurora 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 specifying filters and ordering, and limit results.
If you’re looking to export data in bulk, 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
Here’s the tricky part: for every table in your Amazon Aurora database, you need a matching table in Redshift to receive the data. Thankfully, Redshift’s syntax is based on the syntax of Postgres, another relational database built on the SQL standard. If you run MySQL command like SHOW CREATE TABLE, you will receive syntax that will serve as a good starting point for creating a Redshift table that can receive the data.
That said, however, it’s not a one-to-one match. You’ll need to familiarize yourself with the Redshift CREATE TABLE statement and the implications of, for example, selecting certain sort keys at creation time. Redshift is a very different beast than Amazon Aurora and it’s important that you appreciate the performance implications that your table structure can create.
Loading data into Redshift
When you've identified all the columns you want to insert, use the Reshift CREATE TABLE statement to make a table in your data warehouse to receive the data.
Now you can replicate your data. It may seem as if the easiest way to do that (especially if there isn't much of it) is to build INSERT statements and add data to your table row by row. If you have any experience with SQL, this probably will be your first inclination. But beware! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, you should instead load the data into Amazon S3 and then use the Redshift COPY command to import it into Redshift.
Keeping Amazon Aurora data up to date
At this point you’ve coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will 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 and resource-intensive.
Instead, identify key fields that your script can use to bookmark its progression through the data and use to 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 Aurora.
And remember, as with any code, once you write it, you have to maintain it. If Aurora sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.
Other data warehouse options
Redshift is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Google BigQuery, PostgreSQL, or Snowflake, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To BigQuery, To Postgres, To Snowflake, and To Panoply.
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 Redshift data warehouse.