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University of The Cumberlands Data Management Computer Science Paper

University of The Cumberlands Data Management Computer Science Paper


Introduction to Data Warehousing:

  • Explanation of what a data warehouse is.
  • Key objectives and benefits of data warehousing.
  • Historical context and evolution of data warehousing.
  • Basic components and terminology (e.g., data marts, OLAP, OLTP).
  • The role of data warehousing in Business Intelligence.

2. Data Warehouse Architecture:

  • Overview of data warehouse architecture layers (e.g., source, data staging, data warehouse, data marts).
  • Explanation of different architectural approaches (e.g., Kimball vs. Inmon).
  • Key considerations for designing a data warehouse architecture.
  • Scalability, performance, and maintenance aspects.

3. ETL (Extract, Transform, Load) Processes:

  • Definition and purpose of ETL processes.
  • Detailed explanation of each ETL stage (extract, transform, load).
  • Tools and technologies commonly used for ETL.
  • Best practices for ETL process design.
  • Challenges and solutions in ETL.

4. Data Modeling for Data Warehousing:

  • Importance of data modeling in data warehousing.
  • Overview of different data modeling techniques (e.g., star schema, snowflake schema).
  • Comparison of dimensional modeling vs. normalized modeling.
  • Steps involved in data modeling for a data warehouse.
  • Case studies or examples of effective data modeling.

5. Data Quality and Data Governance:

  • Explanation of data quality issues and their impact on business intelligence.
  • Introduction to data governance and its significance.
  • Strategies for ensuring data quality in a data warehouse.
  • Compliance and regulatory considerations.
  • Role of metadata management in data governance.

6. Data Warehouse Tools and Technologies:

  • Review of popular data warehousing tools and platforms (e.g., Teradata, Amazon Redshift, Snowflake).
  • Ecosystem of technologies supporting data warehousing (e.g., Hadoop, Spark).
  • Considerations when selecting data warehousing tools.
  • Trends in data warehousing technology (e.g., cloud-based solutions, containerization).

7. Data Warehouse Security:

  • Overview of security challenges in data warehousing.
  • Importance of securing sensitive business data.
  • Access control, authentication, and authorization in data warehouses.
  • Encryption and data masking techniques.
  • Data warehouse security best practices.

Each of these topics can be elaborated upon in your research paper and presented in individual slides in your PowerPoint presentation. Remember to include relevant examples, case studies, and visuals to enhance your presentation. Good luck with your assignment!

CS 305 SNHU Secure Software Development Project





For this assignment, you will research a topic relating to Data Warehousing and Business Intelligence. An example of acceptable topics includes: – Please pick any 2-3 topics not more. Along with the paper, please create a PowerPoint presentation which should be at least 10 slides in length not including title or reference slides.

Introduction to Data Warehousing:

Data       Warehouse Architecture:

ETL       (Extract, Transform, Load) Processes:

Data       Modeling for Data Warehousing:

Data       Quality and Data Governance:

Data       Warehouse Tools and Technologies:

Data       Warehouse Security:

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