Data Warehouse

What Is a Data Warehouse?

A data warehouse is a specialized database designed for analytics and reporting rather than transaction processing. It consolidates data from multiple operational systems into a single repository optimized for complex queries, historical analysis, and business intelligence.

Key characteristics:

• Subject-oriented: Organized around business subjects (sales, finance, customers)
• Integrated: Data from multiple sources combined consistently
• Time-variant: Stores historical data for trend analysis
• Non-volatile: Data is stable; not constantly updated like transactional systems

Data Warehouse vs. Database

Aspect	Operational Database	Data Warehouse
Purpose	Run the business	Analyze the business
Operations	Insert, update, delete	Read-heavy queries
Data	Current state	Historical + current
Schema	Normalized (3NF)	Denormalized (star/snowflake)
Users	Applications, transactions	Analysts, reports
Query complexity	Simple, fast	Complex, analytical

Data Warehouse Architecture

Source Layer

Operational systems feeding the warehouse:

• ERP systems
• CRM systems
• Financial applications
• External data sources

Staging Layer

Temporary holding area for incoming data:

• Raw data landing zone
• Data quality checks
• Transformation processing

Integration Layer

Where data is cleaned and combined:

• Master data management
• Business logic applied
• Consistent definitions enforced

Presentation Layer

Where users access data:

• Dimensional models (star schemas)
• Aggregated summaries
• Semantic layer/metrics

Access Layer

Tools for consuming data:

• BI tools (Tableau, Power BI)
• Excel connections
• AI/ML platforms
• Custom applications

Dimensional Modeling

Data warehouses typically use dimensional models:

Fact Tables

Contain measurable business events:

• Sales transactions
• Financial entries
• Inventory movements

Include:

• Measures (amounts, quantities)
• Foreign keys to dimensions
• Transaction dates

Dimension Tables

Contain descriptive attributes:

• Products (name, category, price)
• Customers (name, segment, region)
• Time (date, month, quarter, year)
• Accounts (number, name, type)

Star Schema

Fact table in center, dimensions around it:

        [Product]
            |
[Time]--[Sales Fact]--[Customer]
            |
        [Region]

Modern Cloud Data Warehouses

Cloud warehouses have transformed the landscape:

Snowflake

• Separates storage and compute
• Pay for what you use
• Near-infinite scalability

Google BigQuery

• Serverless architecture
• Built-in ML capabilities
• Strong for large-scale analytics

Amazon Redshift

• Tight AWS integration
• Familiar PostgreSQL interface
• Good for AWS-centric organizations

Databricks

• Unified analytics platform
• Strong for data science
• Lakehouse architecture

Data Warehouse Benefits

Single source of truth: All business data in one place

Historical analysis: Track trends over months and years

Complex queries: Run analytics not possible in operational systems

Consistent metrics: Same definitions used everywhere

Performance: Optimized for analytical workloads

Security: Centralized access controls

Data Warehouse Challenges

Complexity: Requires specialized skills to design and maintain

Cost: Storage, compute, and tooling expenses

Time to value: Months to implement properly

Data quality: Garbage in, garbage out applies

Adoption: Users must learn new tools and processes

How Go Fig Works with Data Warehouses

Go Fig can work with or without a data warehouse:

With existing warehouse: Connect to Snowflake, BigQuery, or Redshift as a source

Without warehouse: Go Fig provides integrated storage and processing

Warehouse output: Deliver processed data to your warehouse

Semantic layer: Add business meaning on top of warehouse data

Excel bridge: Connect warehouse data directly to spreadsheets

Whether you have a data warehouse or not, Go Fig delivers analytics-ready data to finance teams.