This page provides you with instructions on how to extract data from Amazon S3 CSV and analyze it in Amazon QuickSight. (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 QuickSight?
Amazon QuickSight is the AWS business intelligence tool for creating dashboards and visualizations. Users are charged per session only for the time when they access dashboards or reports. QuickSight supports a variety of data sources, such as individual databases (Amazon Aurora, MariaDB, and Microsoft SQL Server), data warehouses (Amazon Redshift and Snowflake), and SaaS sources (Adobe Analytics, GitHub, and Salesforce), along with several common standard file formats.
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 QuickSight
You must replicate data from your SaaS applications to a data warehouse (such as Redshift) before you can report on it using QuickSight. Once you specify 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 choose the schema you want to work with, and a table within that schema. You can add additional tables by specifying them as new datasets from the main QuickSight page.
Using data in QuickSight
QuickSights provides both a visual report builder and the ability to use SQL to select, join, and sort data. QuickSight lets you combine visualizations into dashboards that you can share with others, and automatically generate and send reports via email.
From Amazon S3 CSV to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing Amazon S3 CSV data in Amazon QuickSight 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 Synapse Analytics, 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 Amazon QuickSight. 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 Amazon QuickSight.