Based On The Feedback You Have Received

Based On The Feedback You Have Received With Respect To Your Data Mode

Based on the feedback you have received with respect to your data model and DDL, now is your chance to implement the final changes. Make any needed changes to your ERD and Physical Data Model and submit for final review. Include any SQL needed for the database, the DDL, the DML to manage the customer and employee rows, and the 3 SELECT statements. In addition, submit the star schema and the DDL to create the Star schema for the data warehouse. You want a single Fact table to track all orders with the following dimensions: Time, Customer, Product, Employee. Be sure to include all DDL including primary and foreign keys; feel free to create new or needed primary keys.

Finally, a specific and detailed discussion about the ETL process is to be used to move data from the OLTP environment to the data warehouse. Your submission should include the following: A description of your approach, features of your enhanced ERD, a discussion about handling the M:M relationship between customer and products, the approach used to ensure 3NF, required SQL statements for the database, Star schema for the fact table and its 4 dimension tables, the DDL for the Star schema for the data warehouse, and a description of the ETL process. Add the Data Warehouse Design and discussion about the ETL process to the project template section titled "Web and Data Warehousing and Mining in the Business World." Include a screenshot of the ERD, logical data model from previous assignments, the DDL to create the tables including table definitions and primary and foreign key constraints, 1–2 pages describing the solution, Star Schema DDL, and 2-3 paragraphs about the ETL process. Ensure to include at least 2 credible references.

Paper For Above instruction

The evolution of data management within organizations increasingly emphasizes the transition from traditional OLTP systems to more sophisticated data warehousing solutions. This shift facilitates comprehensive data analysis, strategic decision-making, and enhanced business insights. The current project involves refining the data model based on received feedback, developing a star schema for the data warehouse, and detailing the ETL (Extract, Transform, Load) process that orchestrates data movement from operational systems to analytical repositories.

Enhanced ERD and Data Model Adjustments

The first step in this process was revising the existing Entity Relationship Diagram (ERD) to accurately capture the relationships and data attributes. Key modifications included resolving many-to-many (M:M) relationships between customers and products, which were addressed by introducing an associative (junction) table, typically named Customer_Products. This handling ensures normalization and facilitates easier data aggregation. The ERD was further enhanced to include detailed primary and foreign key constraints, ensuring referential integrity and supporting the star schema architecture. The ERD now clearly delineates entities for customers, products, employees, orders, and the associated dimensions, with explicit relationships mapped.

The Star Schema Design

The star schema centralizes around a fact table, named Orders_Fact, which captures transaction-level data, including order IDs, timestamps, customer IDs, product IDs, employee IDs, and sales amounts. Four dimension tables—Time_Dim, Customer_Dim, Product_Dim, and Employee_Dim—provide descriptive contextual data to analyze orders across various perspectives.

– Fact table: Orders_Fact


CREATE TABLE Orders_Fact (


Order_ID INT PRIMARY KEY,


Time_ID INT,


Customer_ID INT,


Product_ID INT,


Employee_ID INT,


Quantity INT,


Sales_Amount DECIMAL(10,2),


FOREIGN KEY (Time_ID) REFERENCES Time_Dim(Time_ID),


FOREIGN KEY (Customer_ID) REFERENCES Customer_Dim(Customer_ID),


FOREIGN KEY (Product_ID) REFERENCES Product_Dim(Product_ID),


FOREIGN KEY (Employee_ID) REFERENCES Employee_Dim(Employee_ID)


);

Dimension tables are designed to be denormalized, containing attributes like customer name, segment, product name, category, employee name, and department, enabling fast querying and analysis. Ensuring the schema is in 3NF is achieved during the normalization of source tables and maintaining isolated dimension attributes to prevent redundancy.

ETL Process Description

The ETL process is pivotal in transferring data from OLTP sources to the data warehouse, requiring a systematic approach to extract relevant data, transform it into a suitable format, and load it into the star schema, ensuring data quality and integrity. The extraction phase involves querying operational databases, capturing incremental changes, and staging the data in temporary storage. During transformation, data cleansing, deduplication, and conforming dimension attributes—such as date formats and categorical labels—are performed. The process also involves handling many-to-many relationships, specifically between customers and products, by populating the Customer_Products associative table, which effectively resolves M:M relationships in analytical contexts.

Loading involves inserting the transformed data into the target star schema, with careful management of primary and foreign key constraints to maintain referential integrity. Periodic refreshes can be scheduled depending on business needs, often using incremental updates to optimize performance. Automation of ETL steps through scripting and scheduling tools ensures consistency, timeliness, and minimizes manual intervention.

The ETL architecture employs An ETL tool such as Informatica or Talend to streamline the process, incorporating data validation and audit mechanisms. The overall design ensures that data is accurate, consistent, and ready for multidimensional analysis supporting business intelligence activities.

Conclusion

By finalizing the entity-relationship adjustments, developing a star schema, and implementing a robust ETL process, organizations can significantly enhance their analytical capabilities. The structured approach supports comprehensive data analysis, enabling better decision-making. Ongoing refinement of these processes, guided by feedback and emerging requirements, will ensure that the data warehouse remains a vital asset for strategic insights.

References

  • Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. Wiley.
  • Inmon, W. H. (2005). Building the Data Warehouse. Wiley.
  • Informatica Corporation. (2020). Data Warehouse Design and Implementation Best Practices. Retrieved from https://www.informatica.com
  • Loshin, D. (2019). Mastering Data Modeling and Design: From Concept to Implementation. Elsevier.
  • Chaudhuri, S., & Dayal, U. (1997). An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26(1), 65-74.

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