Scalable Data Architecture for Analytics Organizations
An educational reference covering data lakes, warehouses, pipeline design, governance layers, and integration patterns — structured for Canadian enterprises building analytics capabilities.
-- Data architecture layers
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Architecture Topics
Six Core Architecture Domains
Structured reference material on the fundamental components of modern data architecture.
Data Lakes
Centralized repositories for structured, semi-structured, and unstructured data at scale — including storage formats, partitioning strategies, and lake house patterns.
Explore topic →Data Warehouses
Optimized relational stores for analytical workloads — covering dimensional modeling, slowly changing dimensions, schema design patterns, and query optimization.
Explore topic →Pipeline Design
Patterns and principles for building data pipelines — batch, micro-batch, and streaming architectures — with considerations for reliability, idempotency, and observability.
Explore topic →Governance Layers
Data cataloging, lineage tracking, access control, and quality management — the governance infrastructure that makes data assets discoverable and trustworthy.
Explore topic →Integration Patterns
Enterprise integration patterns for connecting data sources — event-driven, API-based, and file-based approaches — and trade-offs between synchronous and asynchronous models.
Explore topic →Reporting Foundations
The semantic layer, metric definitions, and reporting architecture that bridge raw data to business dashboards — covering BI tools, semantic models, and self-service analytics.
Explore topic →Architecture Is a Decision, Not a Default
Every architectural choice involves trade-offs between scalability, cost, governance, and operational complexity. The reference guides on this site help practitioners understand the options.
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