Which sequence best describes building a reporting data mart from a Clarity OLTP model?

Which sequence best describes building a reporting data mart from a Clarity OLTP model?

• A. Create a normalized schema, implement batch processing only, avoid KPIs.
• B. Use a flat file approach with manual updates.
• C. Create a single wide table, never update, ignore grain.
• D. Build a dimensional model with facts and dimensions, implement ETL/ELT to populate the mart, choose appropriate grain, and optimize for common KPIs.

Answer: (D)

Dimensional modeling with a defined grain and an ETL/ELT process is the right approach to building a reporting data mart. In this setup you structure data into facts, which hold the measurable events and numeric results, and dimensions, which provide the context you need for analysis (such as time, product, customer, and location). Defining the grain is essential because it specifies the level of detail each fact row represents, which in turn determines what you can accurately aggregate and which KPIs you can compute without double counting or losing important detail. ETL or ELT processes then populate the mart from the Clarity OLTP source, performing necessary transformations and data quality checks so that data from different sources aligns (data conformance) and supports reliable, fast analytical queries. This approach enables meaningful KPI calculations, efficient filtering and grouping, and clean, scalable reporting. The other options miss key aspects: using a normalized OLTP-like schema for reporting leads to complex joins and slower queries; relying on flat files with manual updates is error-prone and not scalable; a single wide table with no updates and no defined grain prevents valid aggregation, trend analysis, or KPI derivation because it sacrifices both context and measure integrity.

Dimensional modeling with a defined grain and an ETL/ELT process is the right approach to building a reporting data mart. In this setup you structure data into facts, which hold the measurable events and numeric results, and dimensions, which provide the context you need for analysis (such as time, product, customer, and location). Defining the grain is essential because it specifies the level of detail each fact row represents, which in turn determines what you can accurately aggregate and which KPIs you can compute without double counting or losing important detail. ETL or ELT processes then populate the mart from the Clarity OLTP source, performing necessary transformations and data quality checks so that data from different sources aligns (data conformance) and supports reliable, fast analytical queries. This approach enables meaningful KPI calculations, efficient filtering and grouping, and clean, scalable reporting.

The other options miss key aspects: using a normalized OLTP-like schema for reporting leads to complex joins and slower queries; relying on flat files with manual updates is error-prone and not scalable; a single wide table with no updates and no defined grain prevents valid aggregation, trend analysis, or KPI derivation because it sacrifices both context and measure integrity.