Practice: Cloud Services Platform

Here's a high-level dimensional model for the cloud services platform:

Fact Tables:

• Resource_Usage_Fact
• Billing_Fact
• Service_Performance_Fact

Dimension Tables:

• Date_Dim
• Time_Dim
• Customer_Dim
• Service_Dim
• Resource_Type_Dim
• Region_Dim

Fact Table Schemas:

Resource_Usage_Fact (
    date_key (FK),
    time_key (FK),
    customer_key (FK),
    service_key (FK),
    resource_type_key (FK),
    region_key (FK),
    usage_quantity,
    usage_unit
)

Billing_Fact (
    date_key (FK),
    customer_key (FK),
    service_key (FK),
    resource_type_key (FK),
    region_key (FK),
    billed_amount,
    discount_amount
)

Service_Performance_Fact (
    date_key (FK),
    time_key (FK),
    service_key (FK),
    region_key (FK),
    availability_percentage,
    average_latency,
    error_rate
)

Explanation

Core Business Processes:

1. We've identified three main processes: resource usage, billing, and service performance. Each of these corresponds to a fact table in our model.
2. Granularity:
   • Resource_Usage_Fact: Hourly grain per customer, service, resource type, and region. This allows for detailed usage analysis.
   • Billing_Fact: Daily grain per customer, service, resource type, and region. This supports billing and cost optimization analysis.
   • Service_Performance_Fact: Hourly grain per service and region. This provides detailed performance metrics.
3. Dimensions:
   • Date_Dim and Time_Dim: Support time-based analysis.
   • Customer_Dim: Contains customer attributes including industry and service tier for segmentation.
   • Service_Dim: Stores information about AWS services.
   • Resource_Type_Dim: Captures details about resource types (compute, storage, network).
   • Region_Dim: Represents AWS regions for geographical analysis.
4. Facts:
   • Resource_Usage_Fact: usage_quantity and usage_unit for detailed resource usage analysis.
   • Billing_Fact: billed_amount and discount_amount for billing analysis and cost optimization.
   • Service_Performance_Fact: availability_percentage, average_latency, and error_rate for performance analysis.
5. Slowly Changing Dimensions:
   • Customer_Dim could be a Type 2 SCD to track changes in customer attributes, especially service tier changes.
   • Service_Dim might be Type 2 to track changes in service offerings and pricing tiers.
6. Additional Considerations:
   • The model allows for analysis of resource usage and billing across different services, customers, and regions.
   • Customer growth and churn can be derived from changes in the Billing_Fact table over time.
   • Service adoption patterns can be analyzed by joining Resource_Usage_Fact with Customer_Dim and looking at usage over time by industry.

This model supports all the required analyses:

• Resource usage can be tracked and analyzed using Resource_Usage_Fact.
• Billing and cost optimization opportunities can be identified using Billing_Fact.
• Service performance is monitored through Service_Performance_Fact.
• Customer growth and churn across service tiers can be derived from Billing_Fact and Customer_Dim.
• Service adoption patterns across industries can be analyzed using Resource_Usage_Fact joined with Customer_Dim.

The model balances the need for detailed usage and performance data with the ability to perform billing analysis. It's also flexible enough to accommodate future requirements, such as new services or pricing models.