Please I need this done in 12 hours . If you cant dont Email me.  This is a conclusion of a bigger project and i need it completed. 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: 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: 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.” Purchase the answer to view it

Title: Data Warehouse Design and ETL Process in Web and Data Warehousing and Mining in the Business World

Introduction:
In the present-day business landscape, organizations accumulate vast amounts of data from various sources. To effectively analyze this data and gain valuable insights, businesses often require a data warehousing solution. A data warehouse is a centralized repository that integrates and stores data from different operational systems. This comprehensive paper aims to provide an overview of the data warehouse design and the ETL (Extract, Transform, Load) process for moving data from the online transaction processing (OLTP) environment to the data warehouse.

Data Warehouse Design:
The foundation of a data warehouse is its design. The design is crucial for efficiently managing and organizing the data to support complex analysis. In this project, a star schema design is proposed as it offers simplicity and ease of querying. A star schema consists of a centralized fact table linked to multiple dimension tables.

The single fact table in the proposed data warehouse will track all orders, providing a comprehensive view of the business’s sales transactions. This fact table will serve as the core of the data warehouse, and it will be associated with the following dimensions:

1. Customer Dimension: This dimension captures information related to customers, such as customer ID, name, address, and contact details.
2. Product Dimension: This dimension encompasses details about the products, including product ID, name, description, and price.
3. Time Dimension: This dimension captures time-related information, such as dates, days, months, and years, enabling temporal analysis.
4. Employee Dimension: This dimension holds information about the employees involved in the sales process, including employee ID, name, position, and contact details.

Each dimension table will be linked to the fact table through appropriate key attributes, namely primary and foreign keys, ensuring data integrity and consistency. Further, additional primary keys may be created as needed to enhance the efficiency of data retrieval and integrity in the data warehouse.

ETL Process:
The ETL process is a series of tasks involved in extracting data from the OLTP environment, transforming it to conform to the data warehouse schema, and finally loading it into the data warehouse. The ETL process is integral to the success of a data warehouse, as it ensures the accuracy, consistency, and reliability of the data.

The ETL process for this project will consist of the following steps:

1. Extraction: In this step, data will be extracted from various OLTP systems, such as sales systems, customer relationship management (CRM) systems, and inventory systems. This data will be collected in its raw form, including all relevant attributes.

2. Transformation: Once the data is extracted, it will undergo a series of transformation tasks. These tasks may include data cleaning, data integration, data enrichment, data filtering, and data aggregation. The transformed data will be converted into a format suitable for storage in the data warehouse.

3. Load: The transformed data will be loaded into the data warehouse, specifically into the appropriate star schema tables. This step involves mapping the source data to the target data warehouse schema, performing any necessary data conversions and validations.

4. Validation: The loaded data will be validated against predefined rules and constraints to ensure its integrity and accuracy. Any discrepancies or errors will be identified and resolved, ensuring the quality of the data stored in the data warehouse.

Conclusion:
In conclusion, the design of a data warehouse plays a crucial role in efficiently organizing and managing data for analysis. The proposed star schema design, with a single fact table and associated dimensions, provides a solid foundation for the data warehouse in this project. Additionally, the ETL process ensures the accurate and reliable transfer of data from the OLTP environment to the data warehouse. By following these design and ETL practices, businesses can harness the power of their data and make informed decisions to enhance their performance and competitiveness.

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