This page provides you with instructions on how to extract data from Amazon RDS and analyze it in Superset. (If the mechanics of extracting data from Amazon RDS 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 RDS?
Amazon RDS (relational database service) lets users spin up cloud-based database instances without worrying about infrastructure provisioning or software maintenance or many of the administrative tasks involved in running a database on premises.
Cloud platforms can scale up or down quickly to meet changing demands. RDS takes advantage of that capability to let users add database instances to as needed. It offers automatic backup and recovery for database instances, and can replicate data across multiple zones for high availability.
RDS supports six different database engines: Amazon Aurora, PostgreSQL, MySQL, MariaDB, Oracle Database, and Microsoft SQL Server.
What is Superset?
Apache Superset is a cloud-native data exploration and visualization platform that businesses can use to create business intelligence reports and dashboards. It includes a state-of-the-art SQL IDE, and it's open source software, free of cost. The platform was originally developed at Airbnb and donated to the Apache Software Foundation.
Getting data out of Amazon RDS
The most common way to get data out of any database is to write SQL SELECT queries. As part of any query you can join tables, specify filters, and sort and limit results.
Loading data into Superset
You must replicate data from your SaaS applications to a data warehouse before you can report on it using Superset. Superset can connect to almost 30 databases and data warehouses. Once you choose a data source you want to connect to, you must specify a host name and port, database name, and username and password to get access to the data. You then specify the database schema or tables you want to work with.
Keeping Amazon RDS 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.
The key is to build your script in such a way that it can identify incremental updates to your data. You can identify key fields that your script can use to bookmark its progression through the data, and 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 your database.
From Amazon RDS to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing Amazon RDS 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 RDS to Redshift, Amazon RDS to BigQuery, Amazon RDS to Azure Synapse Analytics, Amazon RDS to PostgreSQL, Amazon RDS to Panoply, and Amazon RDS 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 RDS with Superset. With just a few clicks, Stitch starts extracting your Amazon RDS 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.