Building a Financial Analytics Lakehouse

Building a Financial Lakehouse involved creating a unified data platform to consolidate financial data from over 10 SAP systems. By leveraging modern data engineering practices, the project enabled seamless transitions between raw data ingestion and actionable insights, ensuring transparency and efficiency in financial reporting.

Data Sources

Ingestion

Storage

Visualization

Data Sources:

The team aggregated data from more than 10 SAP systems, including master data and transactional tables. This comprehensive approach ensured the availability of all necessary data for in-depth analysis and reporting.

Ingestion:

Data processing pipelines were built using Spark SQL, enabling smooth transitions between the bronze, silver, and gold layers. These pipelines were designed for scalability, reliability, and optimal performance.

Storage:

Delta Lake was utilized to store and manage data, providing scalability, consistency, and robust data governance. The team ensured that the storage layer supported both historical data analysis and real-time processing needs.

Visualization:

Data was prepared and delivered in formats compatible with tools like Power BI and Azure Synapse Analytics. These visualizations offered stakeholders actionable insights, facilitating better decision-making across financial operations.