Automated Data Analytics on AWS

About this example

AWS is central to modern cloud architectures, and this example provides a comprehensive overview of a robust data analysis setup. Here’s a breakdown of the example:

• Ingress (Data Connectors): Shows various data sources like JSON and CSV files, AWS services (S3, DynamoDB), and Google services (Analytics, BigQuery).
• Automated Data Analysis on AWS: Details the process flow from source connectors through ETL (AWS Glue) to data product creation.
• Core Services: Outlines governance, query services, and notification mechanisms required for a secure and efficient data analysis environment.
• Frontend and Egress: Illustrates how data is presented to end-users via tools like Power BI, and how it interacts with front-end services like static websites and APIs.

When to use

• Data Workflow Design: When structuring a cloud-based data pipeline that requires coordination of various AWS services and external data sources.
• Compliance and Governance: When your team needs to understand and implement data governance and compliance within AWS.
• Educational Resource: When onboarding new team members to the AWS ecosystem, this diagram can serve as a visual aid for understanding the data flow and services involved.

How to use

1. Start editing: Duplicate example file and double-click on the diagram.
2. Customize diagram: Add / modify nodes, groups, relationships using your chosen diagram-as-code syntax. Use icons where possible to make the diagram more intuitive.
3. Customize layout: Drag elements on the diagram directly on the canvas to manipulate the layout.
4. Collect feedback: Share the file with collaborators and encourage them to use the comments feature for feedback.