Amazon S3 CSV to Superset

This page provides you with instructions on how to extract data from Amazon S3 CSV and analyze it in Superset. (If the mechanics of extracting data from Amazon S3 CSV 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 S3?

Amazon S3 (Simple Storage Service) provides cloud-based object storage through a web service interface. You can use S3 to store and retrieve any amount of data, at any time, from anywhere on the web. S3 objects, which may be structured in any way, are stored in resources called buckets. One common use is to store files in comma-separated values (CSV) format, in which each record consists of multiple values separated by commas.

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 CSV data out of S3

AWS has both a REST API and command-line utilities that you can use to get at resources stored in the platform. To retrieve objects you need to know the object and host names, as well as your AWS authorization information.

Preparing CSV data

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in each table, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them.

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.

From Amazon S3 CSV to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Amazon S3 CSV 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 S3 CSV to Redshift, Amazon S3 CSV to BigQuery, Amazon S3 CSV to Azure SQL Data Warehouse, Amazon S3 CSV to PostgreSQL, Amazon S3 CSV to Panoply, and Amazon S3 CSV 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 S3 CSV with Superset. With just a few clicks, Stitch starts extracting your Amazon S3 CSV 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.