Data warehouse requirements

  • What are the necessities of data warehousing?

    Data Warehouse Basics
    The need for Data Warehouse is to generate reports, feed data to Business Intelligence (BI) tools, forecast trends, and train Machine Learning models.
    Data Warehouse stores data from multiple sources such as APIs, Databases, Cloud Storage, etc., using the ETL (Extract Load Transform) process..

  • What are the needs of data warehouse?

    Data Warehouse Basics
    The need for Data Warehouse is to generate reports, feed data to Business Intelligence (BI) tools, forecast trends, and train Machine Learning models.
    Data Warehouse stores data from multiple sources such as APIs, Databases, Cloud Storage, etc., using the ETL (Extract Load Transform) process..

  • What is requirement gathering in data warehouse?

    DATA WAREHOUSING REQUIREMENTS GATHERING METHODS 1.
    Interviews This method is where you interview two or three persons of the company and ask for information that would be of help in creating a data warehouse.
    Some tips on the types of questions to be asked in the interviews follow..

  • Data requirements definition establishes the process used to identify, prioritize, precisely formulate, and validate the data needed to achieve business objectives.
    When documenting data requirements, data should be referenced in business language, reusing approved standard business terms if available.
Key Requirements
  • Big Data Integration.
  • Self-Service ETL.
  • Data Quality.
  • Data Security.
  • Data Governance.
  • Data Storage and Backup.
  • Data Retrieval.
  • Data warehouse requirements engineering.
  • Data warehouse solution conceptualization and platform selection.
  • Data warehouse solution architecture design.
  • Data warehouse system analysis.
  • Data modeling and ETL/ELT design.
  • Data warehouse solution development.
  • Data warehouse quality assurance and launch.

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