Life Cycle

Updated Nov 27, 2021 ·

Overview

A data warehouse goes through key stages: planning, implementation, and maintenance. Each phase involves different roles and tasks.

This phase is all about understanding what data is needed and how it will be structured.

Gather Requirements
- Identify data needs and users.
- Example: A data analyst and a data scientist use the warehouse for reports and machine learning.
Data Modelling
- Plan how data will be structured and linked.
- Example: Data engineers build pipelines, database admins manage source systems.

Now that planning is done, it’s time to build and set up everything.

Build ETL Pipelines
- Extract, transform, and load (ETL) data into the warehouse.
- Expected output: A cleaned dataset ready for analysis.
Develop BI Tools
- Set up reporting tools like Power BI or Tableau.
- Example: Connecting a BI dashboard to query the warehouse.

This phase ensures that everything keeps running smoothly and meets business needs.

Modify & Optimize
- Update table structures as needed.
- Make any required modifications.
Test & Deploy
- Analysts and Data Scientists consult on BI tool setup.
- Data Engineers deploy the data warehouse.
- Analysts validate data, engineers push updates.

Life Cycle Step	Analysts	Data Scientist	Data Engineers	Database Administrators
Business Requirements	✔	✔
Data Modeling	✔	✔	✔	✔
ETL Design & Development			✔	✔
BI Application Development	✔	✔
Maintenance			✔
Test & Deploy	✔	✔	✔