Choose One Of The Data Warehousing Data Mart Dimensions

Choose One Of The Data Warehousing Data Mart Dimensional Models List

Choose one of the data warehousing / data mart dimensional models listed below. Copy the data model into a MS Word document. In your MS Word document. (You can copy the data model graphic by right-clicking on it. On PCs, the keyboard combination of ctrl-V is the shortcut command for pasting.) In the MS Word document, 1) identify the fact table 2) identify the dimension tables, 3) for each dimension table, identify the attribute(s) that would be matched to an attribute in the fact table, and 4) list four queries that could be produced from this data model. For example, the queries from a data model of real estate sales that could typically be produced by matching a fact table to one or more dimension tables include: a) list of properties sold by each real estate agent, b) total properties sold by state, c) types of properties sold by month and year, d) dollar value of properties sold by each real estate company, e) total homes sold by real estate agent by company by year.

Paper For Above instruction

Introduction

Data warehousing and data mart frameworks are crucial for effective business intelligence, as they facilitate data analysis and reporting across various domains. One common approach involves the use of dimensional models, which organize data into fact and dimension tables for efficient querying. This paper focuses on analyzing a selected dimensional model, specifically the “Dimensional Model for Logistics & Shipments,” to identify its key components and demonstrate how it can be utilized for various business queries.

Selected Data Model: Dimensional Model for Logistics & Shipments

The chosen data model is designed to analyze logistics activities and shipment processes within a supply chain. The model comprises a central fact table and several related dimension tables that provide descriptive context for the logistics data.

Identification of the Fact Table

The primary fact table in this model is the Shipment Facts table. It records individual shipment transactions and contains measurable data such as shipment quantity, weight, cost, and delivery time.

Identification of Dimension Tables

The related dimension tables include:

  • Date Dimension
  • Location Dimension
  • Carrier Dimension
  • Product Dimension
  • Customer Dimension

Attributes Matched Between Fact and Dimension Tables

For each dimension table, specific attributes are linked to the fact table:

  • Date Dimension: Shipment Date (matches the date attribute in the fact table)
  • Location Dimension: Origin and Destination Locations
  • Carrier Dimension: Carrier ID or Name
  • Product Dimension: Product ID or Name
  • Customer Dimension: Customer ID or Name

Sample Queries Derived from the Data Model

Using this data model, logistics analysts can generate insightful queries such as:

  1. What is the total shipment weight by carrier and month?
  2. Which regions have the highest shipment volumes for specific products?
  3. How do delivery times vary across different carriers and locations?
  4. What are the shipment costs associated with each customer over time?

Conclusion

The Dimensional Model for Logistics & Shipments provides a structured method to analyze shipment data effectively. By clearly identifying fact and dimension tables and their relationships, organizations can generate valuable insights into logistics performance, optimize route planning, and improve customer satisfaction through data-driven decisions. Incorporating such models into a data warehouse enhances operational efficiency and strategic planning.

References

  • Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. John Wiley & Sons.
  • Inmon, W. H. (2005). Building the Data Warehouse. John Wiley & Sons.
  • Golfarellli, M., & Rizzi, S. (2009). Data Warehouse Design. In Data Warehouse Design: modern principles and methodologies. Elsevier.
  • Larson, R. (2010). Logistics and Supply Chain Management. Supply Chain Management Review.
  • Rud, R. L. (2017). Evolution of Data Warehouse Architectures. Journal of Data Warehousing.
  • Chen, M., & Li, Q. (2015). Optimizing Logistics Data Analytics Using Dimensional Modeling. International Journal of Logistics Research and Applications.
  • AutoData. (2021). Introduction to Data Warehouse Architectures. Retrieved from https://www.autodata.com/data-warehouse-overview
  • Kimball, R. (2008). The Data Warehouse Lifecycle Toolkit. Morgan Kaufmann.
  • Chaudhuri, S., & Dayal, U. (1997). An Overview of Data Warehousing and Business Intelligence Technologies. ACM SIGMOD Record.

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