Here is e
My answer :
Exactly why the interviewers pushed back on that specific follow-up response and asked for your
methodology:1. You Didn't Explicitly Define the "Grain"
- The Issue: "Grain" is not a table of columns; it is a clear, concise statement of what one single row in your fact table represents.
- What they wanted to hear: Instead of going straight to drawing columns, you must state the grain in a sentence. For example: "The grain is one row per loan disbursal event."
2. The Attributes Don't Support Your Sample KPI
- The Issue: Your sample KPI is "How many loans are dispersed in week, month". But look at your
auto_loans_facts table attributes: loanid, customerid, term, interest%, loan amt. - Why it fails: There is no Date or Time column listed in your table. Without a
date_id or disbursal_date column in the fact table, you cannot group or filter by week or month to calculate that KPI.
3. Mixing Dimensions into the Fact Table
- The Issue: You put
term and interest% inside the fact table. - The Methodology Violation: According to Kimball methodology, a fact table should only contain Foreign Keys (to connect to dimensions) and Numeric Measures (to aggregate).
term and interest% are descriptive attributes that belong in your loan dimension table.
How to Correctly Present This to an Interviewer
To show them you follow a structured methodology, you should present your sample KPI, Grain, and Fact Table like this:
- Sample KPI: Number of loans dispersed by week/month, and total loan amount disbursed.
- Declared Grain: One row per individual loan transaction.
The Correct Fact Table Attributes:
Instead of your current table, the fact table attributes should be cleanly separated into Keys and Measures:
| Column Name | Type | Description |
|---|
| date_key | Foreign Key | Connects to Date Dimension (Needed for your KPI!) |
| customer_key | Foreign Key | Connects to Customer Dimension |
| loan_key | Foreign Key | Connects to Loan Dimension |
| vehicle_key | Foreign Key | Connects to Vehicle Dimension |
| loan_amt | Fact / Measure | Numeric value (Additive) |
Note: To calculate your KPI ("How many loans"), you do a COUNT(loan_key) grouped by the week/month from your date dimension.
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CORRECT ANSWER ( FROM GOOGLE )
Data Warehouse Architecture Design: Loans Database (Snowflake Schema)
➡️ Step 1: Select the Business Process
The business process is the foundational operational event being measured or tracked.
- Process: Loan Disbursal (the precise point in time when a loan agreement is finalized and funds are released).
➡️ Step 2: Declare the Grain
The grain explicitly establishes what exactly a single, individual row inside your central fact table represents.
- Declared Grain: One row per individual loan issued to a customer.
➡️ Step 3: Define Sample KPIs
These are the performance metrics the business layer needs to calculate using this schema.
- KPI 1: Total number (count) of loans disbursed per week and per month.
- KPI 2: Total dollar volume (sum) of loan amounts issued by vehicle brand.
- KPI 3: Average interest rate offered per customer tier (e.g., VIP vs. Standard).
- KPI 4: Total unique clients served and total profit generated by month.
➡️ Step 4: List of Dimension Tables & Columns
Here is the clean list of all dimensions, including base tables and their normalized snowflake outriggers, along with their columns:
- 📅
Dim_Date (date_key, full_date, day_of_week, week_number, month_name, calendar_year) - 👤
Dim_Customer (customer_key, customer_id, first_name, last_name, address_key, customer_type_key) - 📍
Dim_Customer_Address (address_key, city, state, postal_code) - 🏷️
Dim_Customer_Type (customer_type_key, type_name) - 🚗
Dim_Vehicle (vehicle_key, vin_number, vehicle_year, model_key) - 🚘
Dim_Vehicle_Model (model_key, model_name, brand_key) - 🏢
Dim_Vehicle_Brand (brand_key, brand_name) - 📄
Dim_Loan_Details (loan_details_key, loan_id, loan_term_months, interest_rate_percentage, loan_type_key) - 🗂️
Dim_Loan_Type (loan_type_key, loan_type_name)
📊 Step 5: The Fact-Dimensional Snowflake Model Diagram (Schema Layout)
1. Central Fact Table
Fact_Auto_Loans
| Column Name | Key Type | Data Type | Description |
|---|
date_key | Foreign Key (FK) | INT | Links to Dim_Date (Resolves KPI 1 & KPI 4) |
customer_key | Foreign Key (FK) | INT | Links to Dim_Customer (Resolves KPI 4) |
vehicle_key | Foreign Key (FK) | INT | Links to Dim_Vehicle (Resolves KPI 2) |
loan_details_key | Foreign Key (FK) | INT | Links to Dim_Loan_Details (Resolves KPI 3) |
loan_amount | Fact / Measure | DECIMAL | Numeric, fully additive principal dollar value |
profit_amount | Fact / Measure | DECIMAL | Numeric, fully additive profit dollar value (Resolves KPI 4) |
2. Snowflake Dimension Tables and Outriggers Breakdown
📅 Time Branch
Dim_Date
date_key (PK)full_dateday_of_weekweek_number ➡️ (Calculates weekly loan count KPI)month_name ➡️ (Calculates monthly loan count, client count, and profit KPIs)calendar_year
👤 Customer Hierarchy
Dim_Customer
customer_key (PK)customer_idfirst_namelast_nameaddress_key (FK) ➡️ Links to Dim_Customer_Addresscustomer_type_key (FK) ➡️ Links to Dim_Customer_Type
Dim_Customer_Address (Outrigger)
address_key (PK)citystatepostal_code
Dim_Customer_Type (Outrigger)
customer_type_key (PK)type_name ➡️ (e.g., Retail, Corporate, VIP)
🚗 Vehicle Asset Hierarchy
Dim_Vehicle
vehicle_key (PK)vin_numbervehicle_yearmodel_key (FK) ➡️ Links to Dim_Vehicle_Model
Dim_Vehicle_Model (Outrigger)
model_key (PK)model_namebrand_key (FK) ➡️ Links to Dim_Vehicle_Brand
Dim_Vehicle_Brand (Outrigger)
brand_key (PK)brand_name ➡️ (e.g., Honda, Ford - Calculates brand volume KPI)
📄 Loan Rules Hierarchy
Dim_Loan_Details
loan_details_key (PK)loan_idloan_term_monthsinterest_rate_percentage ➡️ (Calculates average interest rate KPI)loan_type_key (FK) ➡️ Links to Dim_Loan_Type
Dim_Loan_Type (Outrigger)
loan_type_key (PK)loan_type_name
Let me know if you are ready to explore the SQL query execution to build a comprehensive dashboard for this schema or if you want to look at another process like Loan Repayments!
