Redshift

What Is Amazon Redshift?

Amazon Redshift is AWS’s cloud data warehouse service, designed for large-scale data analytics. Launched in 2012, it was one of the first cloud data warehouses and remains popular for organizations heavily invested in the AWS ecosystem.

Key characteristics:

• Columnar storage for fast analytics
• Massively parallel processing (MPP)
• Tight AWS integration
• Multiple cluster configurations
• Serverless option available
• Mature, proven platform

Redshift Architecture

Cluster-Based

Traditional Redshift uses clusters:

• Leader node (query coordination)
• Compute nodes (data storage and processing)
• Node types: Dense Compute (DC) or RA3

Redshift Serverless

Newer serverless option:

• No cluster management
• Automatic scaling
• Pay for compute used
• Simpler pricing

Columnar Storage

• Data stored by column, not row
• Efficient compression
• Fast analytical queries
• Zone maps for query optimization

Redshift for Finance

Common financial use cases:

Financial consolidation

• Multi-entity data centralization
• Historical financial data
• Trend analysis

Operational reporting

• Real-time dashboards
• KPI tracking
• Operational metrics

Customer analytics

• Revenue analysis
• Customer segmentation
• Lifetime value

Cost analysis

• Spend analytics
• Vendor analysis
• Cost allocation

Redshift vs. Other Data Warehouses

Aspect	Redshift	Snowflake	BigQuery
Cloud	AWS only	Multi-cloud	GCP only
Architecture	Cluster/Serverless	Virtual warehouses	Serverless
Pricing	Per-node or serverless	Per-second	Per-query
AWS integration	Native	Via PrivateLink	N/A
Scaling	Resize cluster	Instant	Automatic
Concurrency	Scaling feature	Per-warehouse	High

Redshift Features

Redshift Spectrum

• Query data in S3 directly
• No loading required
• Extend warehouse to data lake

Concurrency Scaling

• Handle query spikes
• Automatic additional capacity
• Pay only when used

Materialized Views

• Pre-computed query results
• Automatic refresh
• Query acceleration

Federated Query

• Query across Redshift, RDS, S3
• Single SQL statement
• Real-time data access

ML Integration

• Redshift ML
• SageMaker integration
• SQL-based predictions

Redshift Pricing

Provisioned clusters:

• Per-node per-hour
• Reserved instances for discounts
• Spectrum charges for S3 queries

Serverless:

• Redshift Processing Units (RPU)
• Per-second billing
• No idle costs

Storage:

• Managed storage pricing
• Automatic tiering

How Go Fig Works with Redshift

Go Fig integrates with Redshift for financial analytics:

Redshift as source:

• Connect to Redshift clusters
• Query tables and views
• Use existing data models

Redshift as destination:

• Load data from ERPs, CRMs
• Build unified data layer
• Support broader analytics

Go Fig value-add:

• Semantic layer over Redshift
• Excel delivery without SQL
• Combine with non-AWS sources
• AI-powered insights

Redshift Best Practices

Performance

• Choose appropriate distribution keys
• Define sort keys for common queries
• Use compression encodings
• Vacuum and analyze regularly

Cost Management

• Right-size clusters
• Use reserved instances for steady workloads
• Leverage Spectrum for cold data
• Consider serverless for variable workloads

Security

• VPC isolation
• Encryption at rest and in transit
• IAM integration
• Column-level access control

Getting Started with Redshift

For finance teams considering Redshift:

1. Assess AWS investment: Already on AWS? Natural fit.
2. Choose deployment: Provisioned vs. Serverless
3. Plan data loading: S3, DMS, Fivetran, etc.
4. Design schema: Distribution, sort keys, compression
5. Select access tools: QuickSight, Go Fig, third-party BI

Go Fig provides a business-friendly layer, making Redshift data accessible through Excel and dashboards without requiring SQL skills or deep AWS knowledge.