Data Warehouse Architecture for Analytics
Design high-performance data warehouses that transform raw data into actionable business intelligence
Analytics Challenges Without Proper Architecture
Slow Query Performance
Reports that used to take seconds now take minutes or hours, frustrating business users and delaying decisions.
Data Inconsistency
Different departments report different numbers for the same metrics, eroding trust in data.
High Operational Costs
Inefficient queries and poor data organization lead to skyrocketing cloud computing bills.
Modern Data Warehouse Architecture
We design cloud-native data warehouses optimized for analytical workloads, BI tools, and machine learning
Cloud-Native Warehouse Platforms
Modern data warehouses leverage cloud platforms like Snowflake, BigQuery, or Redshift that separate compute from storage, enabling elastic scaling and pay-per-use pricing. These platforms handle infrastructure management so you can focus on analytics.
Snowflake
Best for multi-cloud deployments, instant elasticity, and zero-copy data sharing across organizations.
BigQuery
Ideal for petabyte-scale analytics with built-in ML capabilities and tight GCP integration.
Redshift
Perfect for AWS-native architectures with tight integration to S3, Athena, and other AWS services.
Databricks SQL
Unified analytics platform combining data warehousing with data science and ML workflows.
Dimensional Modeling for Analytics
We implement proven dimensional modeling techniques (star schema, snowflake schema) that optimize query performance and make data intuitive for business users. Fact tables store metrics while dimension tables provide context.
Performance Optimization Strategies
Warehouse performance depends on smart data organization, partitioning, and indexing strategies tailored to your query patterns.
Partitioning
Divide tables by date, region, or business unit to allow query pruning and parallel processing.
Clustering
Organize data within partitions based on frequently filtered columns to minimize data scanning.
Materialized Views
Pre-compute expensive aggregations and joins to accelerate common analytical queries.
Compression
Apply columnar compression to reduce storage costs and improve I/O performance.
Transform Your Analytics Infrastructure
Our data architects will evaluate your current setup and design a modern warehouse architecture that scales with your business.
Key Architecture Patterns
1. Data Vault 2.0 Architecture
For enterprises with complex data lineage requirements, Data Vault provides a highly auditable and flexible modeling approach. It separates business keys (hubs), relationships (links), and attributes (satellites) to enable agile schema evolution.
Best for: Heavily regulated industries (finance, healthcare), organizations with frequent source system changes, and scenarios requiring complete historical tracking.
2. Kimball Dimensional Modeling
The Kimball approach focuses on business process-oriented design with conformed dimensions shared across fact tables. This creates intuitive, performant schemas that business users can understand and query directly.
Best for: BI-centric organizations, self-service analytics, and scenarios where query performance and user simplicity are paramount.
3. One Big Table (OBT) Pattern
For cloud warehouses with columnar storage, denormalized "One Big Table" designs can simplify queries and improve performance. Pre-join dimensions with facts to eliminate complex query logic.
Best for: Cloud-native warehouses like BigQuery, ad-hoc analysis workflows, and teams with limited SQL expertise.
4. Lakehouse Architecture
Combine data lake flexibility with data warehouse performance using lakehouse platforms like Databricks or open table formats (Delta Lake, Iceberg). Store structured and unstructured data together while supporting ACID transactions.
Best for: Organizations with both BI and ML workloads, scenarios requiring both batch and streaming, and teams wanting to avoid data duplication between lakes and warehouses.
Implementation Strategy
Phase 1: Assessment & Design (2-4 weeks)
We start by understanding your current data landscape, analytical requirements, and business goals. We document all source systems, data volumes, query patterns, and performance SLAs. This informs our platform selection and architecture design.
Deliverables include a detailed architecture blueprint, data model design, ETL/ELT strategy, and migration plan with timeline and resource requirements.
Phase 2: Foundation Build (4-6 weeks)
We establish the core infrastructure: provision cloud warehouse resources, set up security and access controls, implement data ingestion pipelines, and build the foundational dimensional model. Initial data loads validate the architecture.
Focus is on creating a solid technical foundation with proper monitoring, alerting, and operational processes before adding complexity.
Phase 3: Iterative Enhancement (8-12 weeks)
We progressively add data sources, build out additional subject areas, and optimize performance based on actual query patterns. Regular feedback loops with business users ensure the warehouse meets analytical needs.
This phase includes developing semantic layers, building data marts for specific departments, and integrating with BI tools like Tableau, Power BI, or Looker.
Phase 4: Optimization & Scale (Ongoing)
Continuous monitoring identifies optimization opportunities: query tuning, materialized view creation, partition strategy refinement, and capacity planning. We implement cost optimization strategies to manage cloud expenses as data volumes grow.
Case Study: Financial Services Modernization
The Challenge
A Nordic fintech company was running analytics on a legacy on-premises Oracle data warehouse. Query performance degraded as data volumes grew, and their nightly ETL windows expanded to 14+ hours, delaying morning reports. Cloud costs for their initial migration attempt were 3x projections due to poor architecture.
Our Solution
We designed a Snowflake-based architecture with a hybrid Kimball/Data Vault approach. Core business processes used dimensional modeling for performance, while complex regulatory data used Data Vault for auditability. We implemented incremental loading with CDC, replacing full table reloads.
The semantic layer was built using dbt, providing consistent business metrics across all reporting tools. We set up automated performance monitoring and cost optimization using Snowflake resource monitors and automatic clustering.
Frequently Asked Questions
Should we use a star schema or data vault?
Star schema (Kimball) is ideal for BI-focused use cases where query performance and user simplicity matter most. Data Vault excels in heavily regulated environments or when you need to track complex data lineage. Many organizations use both: Data Vault for the raw data layer and dimensional models for presentation layers.
How do we choose between Snowflake, BigQuery, and Redshift?
Choose based on your cloud ecosystem and specific needs. Snowflake offers the best multi-cloud flexibility and data sharing. BigQuery is ideal if you're on GCP with petabyte-scale data and want built-in ML. Redshift works best for AWS-native architectures. All three perform well for typical enterprise workloads.
What about data warehouse vs data lake vs lakehouse?
Data warehouses are optimized for structured, curated data and SQL-based analytics. Data lakes store raw, unstructured data cheaply. Lakehouses combine both, offering warehouse performance on lake storage. For most enterprises, a lakehouse architecture provides the best flexibility, supporting both BI and ML workloads without data duplication.
How do we ensure data quality in the warehouse?
Implement data quality checks at ingestion (schema validation, null checks), transformation (business rule validation, referential integrity), and consumption (anomaly detection, freshness monitoring). Tools like dbt tests, Great Expectations, or Monte Carlo provide automated quality monitoring. Define clear data ownership and SLAs.
What's the typical cost of a cloud data warehouse?
Small to medium businesses typically spend $2,000-$20,000/month for warehouses processing 1-10TB. Large enterprises with 100TB+ may spend $50,000-$200,000/month. Costs depend on data volume, query frequency, and concurrency. We optimize costs through smart partitioning, clustering, and automated scaling policies.
Ready to Modernize Your Analytics?
Our data architects have designed warehouses processing petabytes for Nordic enterprises. Let's build an architecture that scales with your ambitions.