This Week You Will Continue Your Work On The Project To Eval

This Week You Will Continue Your Work On The Project To Evaluate Highe

This week you will continue your work on the project to evaluate higher education student aid data. You will transform your operational data structure and schema into a data structure and schema for a data warehouse, which will be exclusively used for reporting. Develop a plan to integrate this new data warehouse with an internet application. Include in your plan: A description of the transformation process, a schema diagram identifying the changes needed, and a revised diagram based on instructor feedback. Provide specific integration plans. Document your plan as a 1- to 2-page Microsoft® Word document.

Paper For Above instruction

Implementing an efficient data warehouse integration with a web application requires meticulous planning, encompassing data transformation, schema modifications, and clear operational strategies. This paper outlines a comprehensive plan to transform operational higher education student aid data into a reporting-oriented data warehouse, includes schema changes, and details the integration process with an internet application.

Transformation Process Overview

The first step involves extracting data from the operational systems, which typically contain detailed and transactional data. These source systems often use normalized schemas optimized for transaction processing but inefficient for reporting purposes. Extracting this data involves using ETL (Extract, Transform, Load) tools to gather relevant data, such as student demographics, aid awards, disbursement dates, and program details, into a staging area.

Transformation involves cleaning and converting the data to fit the dimensional model of the data warehouse. This includes standardizing data formats, resolving inconsistencies, and consolidating fragmented data points. For example, transforming multiple address records into a single standardized format, or aggregating disbursement amounts across different time periods. The data is then loaded into the warehouse schema—optimized for reporting and analysis, typically employing fact and dimension tables.

Schema Diagram and Changes

The target schema adopts a star schema model with one central Fact Table, such as 'Aid_Disbursements', surrounded by dimension tables like 'Students', 'Aid_Types', 'Institutions', and 'Programs'. In the original operational schema, data is highly normalized with multiple linked tables; these are denormalized in the warehouse to optimize query performance.

The schema diagram illustrates these changes, showing the fact table linked via foreign keys to each dimension table. Key modifications include the addition of surrogate keys for warehouse consistency and the conversion of categorized fields into dimension attributes. The revised diagram also reflects feedback from Week 2, where normalization was reduced to improve report generation efficiency.

Integration Plan for the Internet Application

Integrating the data warehouse with an internet application necessitates secure, real-time data access or scheduled updates. The plan involves deploying a RESTful API layer that allows the application to query the data warehouse directly. This API will perform the following functions:

- Data Retrieval: Provide endpoints for retrieving reports on student aid disbursements, program participation, and institutional performance.

- Data Refreshing: Schedule nightly ETL jobs to refresh warehouse data, ensuring reports are current.

- Security Measures: Implement authentication and authorization protocols, encrypt data in transit, and maintain audit logs to safeguard sensitive student information.

- User Interface Considerations: Design user-friendly dashboards that connect with the API, allowing users to filter, visualize, and export data effectively.

The architecture supports scalability and maintains data integrity, with the data warehouse serving as a single source of truth for reporting, accessible via the web application.

Conclusion

This plan provides a structured approach to transforming operational data into a reporting-ready data warehouse while facilitating seamless integration with an internet application. By following these outlined steps, higher education institutions can improve data accessibility, support informed decision-making, and enhance transparency for stakeholders.

References

  • Building the Data Warehouse. Wiley Publishing.
  • The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. Wiley.
  • Data Warehouse Design: Modern Principles and Methodologies. IEEE Software.
  • Business Intelligence: The Savvy Manager's Guide. Morgan Kaufmann.
  • The Data Warehouse Lifecycle Toolkit. Wiley.
  • Designing Data-Driven Websites: From Data Warehousing to Visualizations. Apress.
  • Data Mining: Concepts and Techniques. Morgan Kaufmann.
  • Best Practices for Data Warehouse Integration. IBM White Paper.

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