Data Marts are specialized analytical data warehouses built on the Data Vault model. They are designed to provide quick and convenient access to data aggregated and structured for specific business tasks.

Key Principles of Creating Data Marts

  1. Focus on a specific business process or department:
    Data Marts are tailored for a narrow group of users, such as sales, marketing, or finance analysts.
  2. Optimization for analytical queries:
    Data Marts aggregate and transform data from Raw Vault and Business Vault to speed up report generation.
  3. Use of denormalized structure:
    Unlike the normalized structure of Data Vault, Data Marts are often built as "star schemas" or "snowflakes" to improve query performance.

Stages of Generating Data Marts

1. Defining Requirements:

  • Determine which metrics, indicators, and data dimensions are needed by the business.
  • Examples: sales analytics by region, flight load analysis, or customer behavior.

2. Data Selection:

  • Identify which hubs, links, and satellites from Raw Vault or Business Vault contain the necessary information.

3. Data Transformation:

  • Apply business rules, calculate aggregates, or derive indicators.

4. Creating the Data Mart Schema:

  • Structure the data as a star schema or snowflake.

5. Automation and Updates:

  • Configure the process for regular data warehouse updates.

Example: Building a Data Mart for Flight Load Analysis

1. Source Data in Raw Vault:

  • Hub_Passengers: Unique passengers.
  • Hub_Flights: Unique flights.
  • Link_PassengerFlights: Relationship between passengers and flights.
  • Sat_FlightDetails: Flight details (date, departure point, arrival point).

2. Data Transformation in Business Vault:

Calculate the number of passengers on each flight:

Bridge_FlightLoad:

HubFlightKey TotalPassengers LoadDate
101 150 2025-01-02
102 120 2025-01-02

3. Creating the Data Mart:

Structure the data in a star schema format.

  • Fact table (FlightLoad):
      FlightID Date Origin Destination TotalPassengers
      F123 2025-01-02 JFK LAX 150
      F124 2025-01-02 ORD ATL 120
  • Dimensions:
    • Dim_Flights:
        FlightID Airline AircraftType
        F123 Delta Boeing 737
        F124 United Airbus A320
    • Dim_Dates:
        Date DayOfWeek Month Year
        2025-01-02 Thursday 01 2025

Example Queries from the Data Mart

  1. Comparing flight loads by day of the week:

    SELECT DayOfWeek, AVG(TotalPassengers) AS AvgPassengers FROM FlightLoad FL JOIN Dim_Dates DD ON FL.Date = DD.Date GROUP BY DayOfWeek ORDER BY AvgPassengers DESC;

  2. Top 5 busiest flights:

    SELECT FlightID, TotalPassengers FROM FlightLoad ORDER BY TotalPassengers DESC LIMIT 5;

  3. Flight load analysis by airlines:

    SELECT Airline, SUM(TotalPassengers) AS TotalPassengers FROM FlightLoad FL JOIN Dim_Flights DF ON FL.FlightID = DF.FlightID GROUP BY Airline ORDER BY TotalPassengers DESC;

Recommendations for Building Data Marts

  1. Optimize queries:
    Ensure all key queries run quickly by using indexes and aggregates.
  2. Isolate business rules:
    Move complex logic to Business Vault to keep Data Marts clean and lightweight.
  3. Automate updates:
    Use ETL/ELT tools for regular warehouse updates.
  4. Ensure data consistency:
    Data Marts should align with data in Raw and Business Vault.

Conclusion

Data Marts are the final stage of data transformation in Data Vault. They provide users with convenient access to structured, analytics-ready data. Properly designed Data Marts significantly enhance the performance and accuracy of business analysis.