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    Modern Data Architecture Accelerator (MDAA)

    License

    Note: All documentation in this repo is available as rendered/searchable HTML here.

    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.

    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.

    MDAA Business Value

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

    Modern Data Architecture Examples

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

    Modern Data Architecture Examples

    • 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
    From Scratch With MDAA

    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.

    MDAA Working Backwards

    Starter Kit What you get Est. Deploy Time
    Minimal IAM roles, Glue Catalog encryption, LakeFormation settings — build from scratch ~5–10 min
    GenAI Foundation Enterprise-ready Bedrock Agent with RAG and knowledge bases ~10–15 min
    GenAI GAIA Chatbot RAG chatbot backend with document search, auth, and streaming API ~10–15 min
    Basic DataLake Encrypted S3 storage, data catalog, SQL queries, audit trail ~15–20 min
    Basic DataScience Platform Notebook environment with shared data lake and team isolation ~20–30 min
    MLOps Platform Automated train → deploy → monitor pipeline for ML models ~20–30 min
    DataZone Governed Lakehouse Data lake with row/column-level security and a data product catalog ~20–25 min
    SMUS Research Environment Self-service ML platform for multiple research teams ~20–25 min
    Health Data Accelerator Automated pipeline from source databases to a curated data lake ~30–45 min
    SMUS Data Mesh Multi-account data platform with cross-team data sharing ~30–45 min

    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).

    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.

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

    MDAA Capabilities and Services

    Governance Modules
    Data Lake Modules
    Data Ops Modules
    Data Analytics Modules
    AI / Data Science Modules
    Core / Utility Modules

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

    See CONTRIBUTING.md for information on reporting security issues.

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

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

    MDAA can be used and extended in three ways:

    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.

    Build custom data and AI environments using MDAA's reusable CDK constructs. Multi-language support (TypeScript, Python, Java, .NET) for L2 constructs; L3 constructs are currently TypeScript-only.

    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.

    MDAA Usage and Extension

    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 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).

    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.

    This project is licensed under the Apache-2.0 License.

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