Building Data Governance Architecture on AWS

This diagram illustrates an end-to-end architecture designed to establish robust data governance using a suite of Amazon Web Services (AWS) tools. The structure enables organizations to collect, ingest, store, process, analyze, and visualize data in a secure and scalable environment. The entire flow is divided into six major stages, each fulfilling a key function in the data lifecycle.

CartaNova

Jul 7, 2025

Author: HJ Kim

1. Collection

Data is categorized and collected based on structure:


Structured Data

  • Examples: Relational Databases

  • Clearly defined schema; typically managed in RDBMS systems


Semi-Structured Data

  • Examples: CSV, logs, JSON, XML

  • Has a partial or flexible schema


Unstructured Data

  • Examples: Images, video, audio, PDFs

  • No predefined schema; raw media and document formats

2. Ingestion

This stage brings raw data into the AWS ecosystem using the following services:

  • AWS Transfer Family / AWS Storage Gateway
    Securely transfers data from on-premises systems or third-party sources

  • AWS Glue / Amazon Kinesis Firehose / AWS Lambda
    Serverless data ingestion and real-time/batch transformation

  • Amazon SNS / Amazon SQS
    Enables asynchronous message passing and event-driven processing between stages

3. Storage

Once ingested, data is stored in optimized repositories based on usage:

  • Amazon S3
    Scalable object storage for structured, semi-structured, and unstructured data

  • Amazon Redshift / Amazon RDS
    Columnar data warehouse (Redshift) and relational database service (RDS) for analytics and transactional use cases

4. Preparation & Computation

This stage involves data transformation, model training, and advanced analytics:

  • Amazon EMR
    Big data processing using Hadoop/Spark clusters

  • Amazon SageMaker / Personalize / Forecast
    Full-service machine learning platforms to build, train, and deploy AI/ML models for personalization, forecasting, and intelligent recommendations

5. Analysis & Presentation

This layer focuses on extracting insights and making data accessible to users:

  • Amazon SageMaker
    Model experimentation and inference

  • Amazon Athena
    Serverless SQL queries on S3-stored data

  • Amazon OpenSearch Service
    Powerful search and analysis of log data or semi-structured content

  • Amazon QuickSight
    Business Intelligence (BI) dashboards and visual analytics

6. Infrastructure & Environment

This layer ensures secure, observable, and reliable operations of the entire stack:

  • Amazon Managed Grafana / Prometheus
    Metrics visualization and system monitoring

  • Amazon CloudWatch
    Log aggregation, alerting, and observability for AWS services

  • AWS Identity and Access Management (IAM)
    Fine-grained access control and user permission policies

  • Amazon Pinpoint
    Personalized communication, notifications, and user engagement tracking

Summary

This architecture goes beyond just storing or analyzing data. It is designed to:

  • Centralize all organizational data through a unified Data Lake

  • Automate data flow via serverless and event-based services

  • Ensure security, traceability, and compliance

  • Provide ML-ready infrastructure with SageMaker and Forecast integration

  • Enable scalable analytics and enterprise-wide insight generation

This framework provides a comprehensive blueprint for building scalable, secure, and intelligent data governance systems using AWS. If you're preparing for digital transformation, this architecture can serve as a practical foundation for long-term success.

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