- Consolidate data from diverse global sources into a unified system.
- Accelerate the processing and analysis of historical and transactional data.
- Enable data-driven decision-making through real-time insights.
- Data Quality Issues
- Legacy records with inconsistencies.
- Integration challenges from acquired companies.
- Diverse Data Formats and Schemas
- CRM data in CSV format.
- Product catalog in JSON format.
- Transactional data in Parquet format.
- Complex ETL Processes
- Existing workflows were time-intensive and hard to manage.
The project leveraged Azure Databricks to implement a scalable, reliable, and efficient data lakehouse solution:
- Databricks File System (DBFS): Ingested data from multiple sources.
- Delta Lake: Provided ACID transactions and reliability for data tables.
- Bronze, Silver, and Gold Layers:
- Bronze: Raw ingested data with minimal transformation.
- Silver: Cleaned and enriched data with consistent schemas.
- Gold: Analytics-ready data for reporting and insights.
- Databricks SQL: Enabled high-performance querying.
- Power BI: Used for interactive reporting and dashboards.
- Reduced Processing Time: Achieved faster ingestion and transformation of data.
- Improved Inventory Forecasting: Enhanced accuracy with clean, enriched data.
- Real-Time Reporting: Enabled actionable financial insights through Power BI dashboards.
Feel free to raise issues or contribute enhancements to this project. Let’s work together to build robust data solutions!
Azure Databricks Data Engineering Data Lakehouse E-Commerce Delta Lake Power BI