Factors:

• Based on different use cases.
  • We can use Data Marts specifically built for that use case. A financial-related data mart cannot be used for a user session analytical.
  • Use cases:
    • Transactional
    • Analytical
    • Archival
    • Data Warehousing
• Compability to the ecosystem.
  • If the whole system is using AWS’ ecosystem, it is a better idea to use AWS technology than Microsoft’s.
• Types of data: structured, semi-structured, unstructured.
  • For unstructured data, we cannot use RDBMS.
• Is schema predefined?
  • If the schema is predefined, we should use a Data Warehouse and RDBMS rather than a Data Lake with NoSQL.
• Performance requirements, storage requirements, security requirements.
• Whether working with data at rest or streaming data
  • Streaming data like time-series requires Column-based databases for efficient access and process.
• Frequency of data access, and backup options.
  • Lower cost data Repository is preferred for low-frequent access data as well as unimportant data.
• Standards of the organization.
  • The organization may prefer using open-source technologies to closed-source technologies.