Appendix B — Unified Glossary of Data and Cloud Computing Terms

This glossary consolidates the core terms and definitions covered throughout the theoretical and technical modules of the course, including cloud infrastructure, data storage, processing, and governance.

B.1 A

  • Advanced Provisioning: A cloud delivery model in which the user enters into a formal agreement with the cloud service provider and pays a fixed price or is billed monthly.
  • Apache Hadoop: An open-source framework that enables distributed storage and processing of large datasets across clusters of computers using simple programming models.
  • Archival Policy: A guideline defining where, how, and for how long data should be stored after an analytics project has concluded.
  • Audit (Data Audit): A formal and systematic examination of how users access and utilize data, designed to ensure compliance with security policies and identify vulnerabilities.
  • Autoscaling: A cloud service that monitors applications and automatically adjusts compute capacity (scaling up or down) based on real-time demand.

B.2 B

  • Backend Platform: The collection of physical and logical components within a cloud architecture that support system execution (servers, networks, storage).
  • Batch Processing: A data processing method consisting of collecting a large volume of transactions or operations over a period of time and processing them together in a single execution.
  • Business Data Request: A formal question or requirement submitted by a business area that can be answered or resolved through data extraction and analysis.

B.3 C

  • Cloud Data Storage: A technology solution that enables organizations to store, access, and maintain digital data on remote servers managed by a service provider (e.g., Google Cloud Storage).
  • Complex Data Types: Non-primitive data structures (such as Arrays and Structs) used in modern databases like BigQuery to store nested records and optimize query performance on denormalized data.

B.4 D

  • Data Catalog: A centralized inventory of an organization’s data assets providing detailed metadata to help analysts discover, understand, and evaluate the quality of available sources.
  • Data Governance: The set of processes, policies, roles, and standards that ensure the secure, reliable, and efficient use of data within an organization.
  • Data Lake: A storage repository that holds large volumes of raw data in its native format (structured, semi-structured, or unstructured) until needed for analysis.
  • Data Warehouse: A relational database system optimized for data analytics (OLAP) that consolidates cleansed information from multiple transactional systems (OLTP).

B.5 N

  • Normalization: A database design technique that organizes relational tables to minimize data redundancy and enforce referential integrity.

B.6 S

  • Streaming Processing: A data processing method that continuously analyzes and transforms records in real time as they are generated by source systems.