Modern Data Architecture Accelerator (MDAA)

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The Modern Data Architecture Accelerator (MDAA) deploys secure, compliant modern Data and AI architectures on AWS in minutes. Pick a starter kit, fill in your environment details, and deploy — MDAA handles security compliance, encryption, and IAM best practices automatically.

Table of Contents

• Why MDAA
• Uses
• Key Features
• Quick Start
• Available Modules
• Security
• Workshops and Learning Resources
• Using and Extending MDAA
• For Developers
• Contributing
• License

Why MDAA

Building a secure, compliant data and AI platform on AWS typically takes months of architecture design, security reviews, and infrastructure coding. MDAA compresses that into minutes. You get production-ready infrastructure that passes compliance audits on day one — not after weeks of remediation.

• Minutes, not months: Deploy a complete data lake, ML platform, or GenAI backend in 15–45 minutes instead of building from scratch.
• Compliance without compromise: Every module enforces encryption, least-privilege IAM, and audit logging by default. No shortcuts, no security debt.
• Focus on your problem, not plumbing: Spend time on data, models, and business logic — not on writing CloudFormation, debugging IAM policies, or passing security reviews.

Uses

MDAA can be used to build an architecture to solve any variety of data or AI problem, including:

Additionally, MDAA can be used to build complex, multi-domain and multi-account data and AI architectures:

Key Features

• Deploy in minutes, not months: Starter kits go from zero to a working environment in 15–45 minutes. No custom code required.
• Security and compliance built in: All modules enforce encryption, least-privilege IAM, and audit logging. Validated against AWS Solutions, NIST 800-53, HIPAA, and PCI-DSS rulesets.
• Configuration-driven: Define your environment in YAML files. Change what you need, leave the rest as secure defaults.
• Extend as you grow: Start with a kit, then add modules for analytics, AI, governance, or data pipelines as your needs evolve.
• Multi-account and multi-region: Deploy across accounts and regions with built-in cross-account trust.

Without MDAA	With MDAA

Quick Start

Prerequisites

• macOS, Linux, or Windows
• Node.js 22.x and npm 10.x
• AWS credentials configured with appropriate permissions (AWS CLI setup)

1. Pick a Starter Kit

Start by identifying the business problem you need to solve, then select the starter kit that provides the best coverage of required capabilities. Starter kits are just starting points — deploy as-is or modify and extend with additional modules and configurations to match your requirements. If necessary, MDAA deployments can be further customized through code-level escape hatches.

2. Copy the starter kit configs to your own directory:

Copy the contents of your chosen starter kit from starter_kits/<kit-name>/ into your own project directory. These are plain YAML files — no build tools or dependencies required.

cp -r starter_kits/basic_datalake ./my-project
cd ./my-project

Address the # TODO: comments in the config files (org name, account details, CDK Nag suppressions).

3. Follow the starter kit's README

Each kit includes a Deployment section with prerequisites, configuration steps, and deploy commands. See the starter kit's README for details (README will also be in the created starter config directory).

For general deployment guidance, see PREDEPLOYMENT and DEPLOYMENT.

4. Use and extend

Once deployed, start using your new architecture (see the kit's USAGE.md) or enhance it with additional capabilities from the available modules.

Available Modules

MDAA includes 40+ modules for governance, data lakes, pipelines, analytics, AI, and utilities. Browse the full list with documentation at aws.github.io/modern-data-architecture-accelerator.

Security

See SECURITY.md for details on MDAA's security design principles and compliance approach.

See CONTRIBUTING.md for information on reporting security issues.

Workshops and Learning Resources

Self-Paced Workshops

• MDAA Hands-On Workshop: A guided, hands-on workshop that walks you through deploying and configuring MDAA from scratch.

Documentation

Browse the full documentation, module references, and configuration schemas at aws.github.io/modern-data-architecture-accelerator.

• Architecture and Design Guide: Reference architectures and design patterns for MDAA deployments.
• Configuration Guide: How to author MDAA YAML configuration files.
• Customization Guide: How to extend MDAA modules with code-based escape hatches.
• Predeployment Guide: How to prepare your AWS accounts for MDAA deployment.
• Deployment Guide: Step-by-step deployment instructions using the MDAA CLI.

Using and Extending MDAA

MDAA can be used and extended in three ways:

Configuration-Driven Deployment

Deploy compliant, end-to-end data and AI environments using YAML config files and the MDAA CLI. No code required - accessible to all roles, from simple to complex deployments with high compliance assurance.

Code-Driven Custom Environments

Build custom data and AI environments using MDAA's reusable CDK constructs. L2 and L3 constructs are published as TypeScript npm packages under the @aws-mdaa scope.

Workload Integration

Independently developed workloads (CDK or CloudFormation) can leverage MDAA-deployed resources via the standard set of SSM (Systems Manager) parameters published by all MDAA modules.

Metrics Collection

This solution collects anonymous operational metrics to help AWS improve quality and features. For more information, including how to disable this capability, see the CDK version reporting documentation.

For Developers

For detailed guides, see:

• CONTRIBUTING.md - Project architecture, coding guidelines, and pull request process.
• DEVELOPMENT.md - Development environment setup, build process, and tooling.
• TESTING.md - Testing standards, architecture, and coverage requirements.

Full documentation and module reference is available at aws.github.io/modern-data-architecture-accelerator. To generate the docs locally, run mkdocs serve from the project root (requires MkDocs).

Contributing

We welcome contributions from the community. See CONTRIBUTING.md for guidelines on how to get started, set up your development environment, and submit pull requests.

License

This project is licensed under the Apache-2.0 License.