To develop a cloud-based data warehouse system that integrates multiple data sources through ETL processes and supports analytical reporting. The project focuses on building scalable data storage, transformation workflows, and structured reporting mechanisms for business intelligence applications.
Understand data warehousing concepts including star and snowflake schemas.
Identify structured and semi-structured datasets for integration.
Design dimensional data models for analytical queries.
Implement ETL pipelines using Python, SQL, or cloud services.
Extract data from CSV files, APIs, and relational databases.
Transform datasets through cleansing, normalization, and aggregation.
Load processed data into a cloud-based warehouse (e.g., AWS Redshift or BigQuery).
Create fact and dimension tables with proper indexing.
Optimize query performance using partitioning and clustering.
Implement scheduling for automated ETL workflows.
Create business intelligence dashboards for sales and revenue analysis.
Ensure data consistency and integrity checks.
Apply access control and data security mechanisms.
Test system performance with large datasets.
Prepare technical documentation and system architecture diagrams.
