Snowflake

What Is Snowflake?

Snowflake is a cloud data platform that provides data warehousing, data lake, data engineering, and data sharing capabilities. Its innovative architecture separates storage from compute, allowing independent scaling and consumption-based pricing.

Key characteristics:

• Cloud-native (runs on AWS, Azure, GCP)
• Separation of storage and compute
• Near-zero administration
• Pay for what you use
• Instant elasticity
• Cross-cloud data sharing

Snowflake Architecture

Storage Layer

• Data stored in proprietary format
• Automatic compression and encryption
• Pay for storage used
• Independent of compute

Compute Layer

• Virtual warehouses for queries
• Multiple warehouses can access same data
• Scale up (bigger) or out (more)
• Auto-suspend when idle

Cloud Services Layer

• Query optimization
• Metadata management
• Authentication and access control
• Infrastructure management

Why Finance Teams Use Snowflake

Centralized data: Single platform for all analytical data

Performance: Fast queries even on large datasets

Scalability: Handle growing data volumes easily

Cost control: Pay only for compute used

Collaboration: Share data across teams and partners

Ecosystem: Integrates with most modern tools

Snowflake for Financial Analytics

Common financial use cases:

Financial reporting

• Consolidated financial data
• Multi-entity reporting
• Historical trending

FP&A

• Budgeting and forecasting
• Variance analysis
• Scenario modeling

Revenue analytics

• Bookings and revenue analysis
• Customer metrics
• Product performance

Operational finance

• Cash flow analysis
• Working capital metrics
• Spend analytics

Snowflake vs. Traditional Data Warehouses

Aspect	Snowflake	Traditional DW
Infrastructure	Managed cloud	Self-managed
Scaling	Instant, elastic	Capacity planning
Pricing	Consumption-based	Upfront/fixed
Administration	Near-zero	Significant
Multi-cloud	Yes	Usually no
Performance	Consistent	Variable

Snowflake Challenges

Cost management: Easy to overspend without monitoring

SQL skills required: Technical skills for direct access

Cold start latency: Warehouses take time to resume

Complex setup: Initial data loading and modeling

Governance: Need clear policies for usage

How Go Fig Works with Snowflake

Go Fig integrates with Snowflake in multiple ways:

Snowflake as source:

• Query data directly from Snowflake
• Use existing data models
• Leverage your Snowflake investment

Snowflake as destination:

• Load data from other sources into Snowflake
• Build unified data layer
• Enable broader analytics

Go Fig value-add:

• Semantic layer on top of Snowflake
• Excel delivery without SQL
• AI-powered insights
• Business-friendly interface

Snowflake Ecosystem

Data loading:

• Fivetran, Airbyte
• Snowpipe (continuous)
• COPY commands (batch)

Transformation:

• dbt
• Snowpark
• Stored procedures

BI tools:

• Tableau, Looker, Power BI
• Mode, Sigma
• Go Fig (Excel-native)

Data science:

• Python/Pandas
• Snowpark ML
• Third-party ML platforms

Getting Started with Snowflake

For finance teams considering Snowflake:

1. Define use cases: What problems will Snowflake solve?
2. Assess data sources: What data needs to flow in?
3. Plan architecture: Databases, schemas, warehouses
4. Consider skills: Do you have SQL expertise?
5. Estimate costs: Model expected consumption
6. Choose tools: BI and analytics layer

Go Fig can provide the business-friendly layer on top of Snowflake, making data accessible to finance teams without requiring SQL skills.