DWH Life Cycle

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Data Ware House Life Cycle Diagram

1) Requirement gathering
  • It is done by business analysts, Onsite technical lead and client

  • In this phase, a Business Analyst prepares business requirement specification(BRS)Document

  • 80% of requirement collection takes place at clients place and it takes 3-4 months for collecting the requirements

2) Analysis
  • After collecting the requirements data modeler starts identifying dimensions, facts & aggregation depending on the requirements
  • An ETL Lead & BA create ETL specification document which contains how each target table to be populated from source
3) System Requirement Specification (SRS)
  • After collection of onsite knowledge transfer, an offshore team will prepare the SRS
  • An SRS document includes software, hardware, operating system requirements
4) Data Modeling
  • It’s a process of designing the database by fulfilling the use requirements
  • A data modeler is responsible for creating DWH/Data marts with the following kinds of schema
  • Star Schema
  • Snowflake Schema

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[Related Article: Data Warehouse Tools]
5) ETL Development
  • Designing ETL applications to fulfill the specifications documents which are prepared in the analysis phase
6) ETL Code review

Code review will be done by the developer

The following activities take place

  • Check the naming standards
  • Check the business logic
  • Check the mapping of source to target
7) Peer Review

A code will be reviewed by a team member

  • Validation of code but not data

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8) ETL Testing

Following tests will be carried out for each ETL Application

  • Unit testing
  • Business Functionality testing
  • Performance testing
  • User acceptance testing
9) Report development environment
  • Design the reports to fulfill report requirement templates/Report data workbook(RDW)

[Related Article: OLTP VS OLAP]

10) Deployment
  • A process of migrating the ETL Code & Reports to a pre-production environment for stabilization
  • It is also known as pilot phase/stabilization phase
11) Production Environment/Go live
  • An active/working environment
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About Author

Ravindra Savaram is a Content Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.

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