Next-gen lakehouse on Azure & Fabric
End-to-end modern data platform for scalable ingestion, transformation, and analytics using ADF, Databricks, and Microsoft Fabric.
Business problem
Gathering and analyzing data from multiple sources was slow and error-prone, limiting the organization's ability to make timely, data-driven decisions.
Architecture overview
high-level architecture diagram and description of how ADF, Databricks, and Fabric work together to ingest, transform, and analyze data.
Technology stack
Azure Databricks, Microsoft Fabric, Azure Data Factory, Azure DevOps, Power BI.
Data flow
Source data is ingested via Azure Data Factory, processed using Databricks, and analyzed through Microsoft Fabric.
Design decisions
The choice of Azure Databricks for processing was driven by its scalability and support for big data workloads. Microsoft Fabric was selected for its seamless integration with other Microsoft services and its capabilities for real-time analytics. Azure Data Factory was used for orchestrating data pipelines, ensuring efficient data movement and transformation.
Security considerations
Financial data security and compliance were paramount, leading to the implementation of robust access controls and encryption mechanisms.
Performance optimization
Performance was optimized by implementing efficient data processing techniques and leveraging the scalability of Azure Databricks.
Scalability considerations
The architecture was designed to scale horizontally, ensuring that the system can handle increasing data volumes and user loads without compromising performance.
Cost optimization
Cost optimization was achieved through efficient resource utilization and the implementation of a pay-as-you-use model.
Monitoring & logging
Monitoring and logging were implemented to track system performance, data quality, and security events.
Error handling
Error handling was implemented to ensure data integrity and system reliability.
Testing strategy
A comprehensive testing strategy was developed to validate data accuracy and system performance.
CI/CD strategy
Continuous integration and deployment strategies were implemented using Azure DevOps to ensure smooth and efficient software releases.
Deployment process
The deployment process involved automated pipelines to ensure consistent and reliable releases across different environments.