Explore AWS QuickSight data sources, supported databases, S3, Athena, and file uploads. Learn how to connect, manage, and optimize datasets easily. Our 24/7 AWS Live Support Team is always here to help you.
Data only becomes useful when you can see it clearly. That’s exactly where AWS QuickSight data sources come into play. Amazon QuickSight makes it simple to pull data from multiple systems, turn it into dashboards, and make decisions without waiting on technical teams.
Once you click “New Dataset” on the QuickSight home screen, you’ll notice how flexible the platform really is. It supports both internal AWS services and popular external databases, giving teams full control over how data flows into their reports.
Let’s walk through the most commonly used AWS QuickSight data sources, step by step.
Overview
Uploading Files from Your System
First, QuickSight allows direct file uploads from your local system. This option is ideal for quick analysis, demos, or one-time reporting.
Supported formats include:
- .csv
- .tsv
- .clf
- .elf
- .xlsx
- .json
After selecting a file, QuickSight instantly detects the schema and previews the data. As a result, you can move straight into visualization without manual setup.
Using Files from Amazon S3
Next, S3 is one of the most widely used AWS QuickSight data sources, especially for large datasets.
To connect S3, you must provide a manifest file. This JSON file tells QuickSight where the data lives and how to read it.
Sample S3 Manifest File
{
"fileLocations": [
{
"URIs": [
"s3://your-bucket/first-file.csv",
"s3://your-bucket/second-file.csv"
]
}
],
"globalUploadSettings": {
"format": "CSV",
"delimiter": ",",
"textqualifier": "'",
"containsHeader": "true"
}
}You can include multiple files, as long as the format stays the same. Consequently, managing large datasets becomes far easier.
Connecting to MySQL and Other Databases
QuickSight also connects directly to relational databases. For example, MySQL is widely used for transactional data and reporting.
To connect, you’ll need:
- DSN name
- Connection type
- Database server name
- Port
- Database name
- Username and password
Once connected, you can import tables or write custom queries.
Supported RDBMS and External Sources
AWS supports a wide range of databases, which makes AWS QuickSight data sources suitable for almost every business setup.
Some popular options include:
- Amazon Athena
- Amazon Aurora
- Amazon Redshift & Redshift Spectrum
- Amazon S3 & S3 Analytics
- MySQL 5.1+
- PostgreSQL 9.3.1+
- Microsoft SQL Server 2012+
- Snowflake
- Teradata
- Presto
- Apache Spark
Therefore, teams rarely need additional connectors or third-party tools.
Turn Your Data Into Dashboards
Using Amazon Athena in QuickSight
Athena is another powerful option. You can either select existing tables or run custom SQL queries.
After validating the connection:
1. Choose the database
2. Select the table or custom SQL
3, Preview the data or jump straight to visualization
As a result, analytics teams get faster insights without data duplication.
Deleting a Data Source Safely
Before deleting any source, always check dependencies. SQL-based sources linked to dashboards may break datasets. However, S3 and SPICE-based datasets remain usable, though refresh options stop.
Always review table size and usage before removal.
Conclusion
From local files to enterprise databases, AWS QuickSight data sources give businesses the freedom to analyze data their way. More importantly, setup is fast, flexible, and built for real-world reporting.
