This page provides you with instructions on how to extract data from Amazon Aurora and analyze it in Superset. (If the mechanics of extracting data from Amazon Aurora seem too complex or difficult to maintain, 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
For every table in your Amazon Aurora database, you'll need a corresponding table in your destination database. Make sure you've pinpointed all of the fields that will be inserted into your destination, and determined the datatypes for each object (i.e. INTEGER, DATETIME, etc.) to make sure they are mapped properly when they get inserted into the new table.
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.
From Amazon Aurora to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing Amazon Aurora data in Superset is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Amazon Aurora to Redshift, Amazon Aurora to BigQuery, Amazon Aurora to Azure SQL Data Warehouse, Amazon Aurora to PostgreSQL, Amazon Aurora to Panoply, and Amazon Aurora to Snowflake.
Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Amazon Aurora with Superset. With just a few clicks, Stitch starts extracting your Amazon Aurora data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Superset.