Appendix D — Bibliography and Additional Resources

This appendix compiles bibliographic references, official tool documentation, and recommended reading cited throughout the course units.

D.1 1. Bibliographic References

  • Attaran, M. (2017). Cloud computing technology: Leveraging the power of the internet to improve business performance. Journal of Systems and Information Technology, 19(3/4), 232-254. (Referenced in cloud computing introduction and storage architectures).
  • Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. John Wiley & Sons. (Core reading for Part I: Dimensional Modeling and Star/Snowflake Schemas).
  • Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0: Step-by-step data mining guide. SPSS. (Foundational analytical methodology for Part II).

D.2 2. Official Documentation and Technology Guides

To dive deeper into the practical tools used in course laboratories, consult the following online resources:

  • Google Cloud Architecture Framework: Architectural design guides for Google Cloud systems. View documentation.
  • BigQuery Quickstart Guide: Reference documentation for analytical storage and large-scale querying. View documentation.
  • PySpark SQL and DataFrame API: Complete reference for distributed data transformations at scale. View documentation.
  • dbt (Data Build Tool) Documentation: Guide for SQL data transformation within the Data Warehouse and orchestration. View documentation.

D.3 3. Datasets and Case Studies

  • TheLook eCommerce Dataset: Public dataset simulating transactions, users, shipments, and inventory for an online clothing store. Hosted in BigQuery Public Data (bigquery-public-data.thelook_ecommerce).
  • BigMart Sales Prediction: Retail sales case study used in Part II laboratory modules for predictive modeling and preprocessing.