MOC: Data Architecture

The complete map of data architecture knowledge, from system blueprints to protocol selection. Six domains cover how to design systems, build pipelines, move data between systems, and choose between competing approaches. Expand any section to browse page contents.

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  ((System Blueprints))
    (warehouses and lakes)
    (lakehouse)
    (data mesh)
    (streaming)
    (metadata architecture)

System Blueprints — Platform Architecture Patterns

domain-system-blueprints

The big structural decisions chosen once and lived with for years — warehouses, lakes, lakehouses, mesh, streaming, open table formats, and context/metadata architecture.

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  ((Data Modeling))
    (dimensional)
    (patterns)
    (analytics models)
    (operational models)

Data Modeling — Structuring Data for Purpose

domain-data-modeling

How to structure data inside each system for analytics, operations, and compliance — dimensional modeling, Data Vault 2.0, wide/flat, and every major modeling paradigm.

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  ((Pipeline Construction))
    (medallion)
    (data flow)
    (functional patterns)
    (idempotency)
    (serialization)
    (backfills)

Pipeline Construction — Building Reliable Data Pipelines

domain-pipeline-construction

Data layering, idempotent operations, transform architecture, data movement topology, serialization, and migration patterns that define how data flows from source to consumer.

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  ((Pipeline Reliability))
    (quality)
    (contracts)
    (testing)
    (error handling)
    (environment strategy)

Pipeline Reliability — Keeping Data Trustworthy

domain-pipeline-reliability

Quality gates, contracts, testing strategy, error handling, and environment management — the patterns that prevent bad data from reaching consumers.

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  ((Integration and Protocols))
    (REST)
    (gRPC)
    (GraphQL)
    (protocol comparison)

Integration & Protocols — How Data Moves Between Systems

domain-integration-and-protocols

API design, protocol selection, and integration patterns covering the full spectrum from REST to gRPC to GraphQL with decision frameworks for each use case.

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  ((Principles and Decisions))
    (five pillars)
    (golden rules)
    (technology matrices)
    (scenario guide)

Principles & Decisions — Choosing the Right Approach

domain-principles-and-decisions

Foundational principles and decision frameworks for every technology and architecture choice — golden rules, selection matrices, and scenario-based guides.

Data Architecture Cross-References

• bronze-layer-loading, silver-transforms, gold-transforms — SQL Server implementations of medallion layers
• airflow-dag-patterns — pipeline scheduling patterns for Airflow
• gcp-scheduling — Cloud Scheduler to Cloud Run patterns
• dbt — full dbt transformation layer section
• dbt-transformation-layer — concise code-heavy dbt overview