Catalog
concept#Data#Analytics#Architecture#Platform

Data Warehouse

A centralized, historical data repository optimized for query, reporting and business intelligence.

A data warehouse is a centralized, structured repository of historical and integrated enterprise data optimized for query and analysis.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

ETL/ELT tools (e.g., dbt, Azure Data Factory)BI and reporting tools (e.g., Power BI, Tableau)Metadata catalogs and lineage tools

Principles & goals

Centralize historical data for reliable analyticsClearly defined semantics and dimensional modelsAutomated data quality checks and lineage
Build
Enterprise, Domain

Use cases & scenarios

Compromises

  • Outdated data models lead to wrong decisions
  • Lack of governance causes inconsistencies
  • Cost explosion from uncontrolled data growth
  • Start with clear, limited domains and extend iteratively
  • Automated tests and CI for data pipelines
  • Document semantics, lineage and responsibilities

I/O & resources

  • Source systems: ERP, CRM, IoT, logs
  • Master data and reference lists
  • Business requirements and KPI definitions
  • Consistent data marts and semantic layers
  • Reports, dashboards and analytical datasets
  • Auditable lineage and archiving

Description

A data warehouse is a centralized, structured repository of historical and integrated enterprise data optimized for query and analysis. It consolidates heterogeneous sources, standardizes schemas and supports business intelligence, reporting and analytical workloads. Common elements include ETL/ELT pipelines, dimensional models and governance.

✔

  • Consistent, enterprise-wide KPI baseline
  • Historization and reproducibility of analyses
  • Efficient aggregation and complex queries

✖

  • High implementation and operational effort
  • Latency between events and available data
  • Limited flexibility for ad-hoc operational workloads

  • Query latency (p95)

    Time in which 95% of queries are answered; important for performance SLAs.

  • Data freshness (time to availability)

    Delay between event occurrence and availability in the warehouse.

  • Data quality errors per period

    Number of detected data quality rule violations within a period.

Retail: sales and inventory analytics

Consolidated warehouse combines POS, inventory and online sales data for demand forecasting and inventory optimization.

Finance: month-end closing and compliance

Financial data is historized and auditable to support regulatory reporting and reconciliations.

Healthcare: research and outcome analysis

Aggregated patient and treatment data enable longitudinal reporting and clinical analyses.

1

Requirements analysis and definition of core KPIs

2

Design the data model and semantics

3

Build ingest, transform and load processes

4

Introduce monitoring, tests and data governance

⚠️ Technical debt & bottlenecks

  • Legacy schemas without clear migration path
  • Untested, undocumented ETL scripts
  • Missing automation for pipeline deployments
data-ingestiontransformationquery-performance
  • Attempting to use the warehouse as a primary streaming store
  • Uncontrolled ingestion of all raw data without schema
  • No versioning of ETL changes and model variants
  • Underestimating effort for data cleansing
  • Forgetting governance when scaling fast
  • Ignoring cost model and storage types
Data modeling (dimensional modeling)ETL/ELT development and orchestrationData governance and data quality management
Scalability for large data volumesQuery and analytics performanceData quality and traceability
  • • Regulatory requirements for storage and access
  • • Budget constraints for storage and processing
  • • Legacy sources with heterogeneous formats