The Challenge
The client collected high volumes of data from ERP, CRM, manufacturing execution systems (MES), IoT devices, and third-party applications. Data was stored across multiple silos with inconsistent formats, making it difficult to maintain data quality and deliver reliable analytics. Existing ETL processes were complex, difficult to scale, and lacked proper governance, resulting in delayed reporting and limited support for advanced analytics initiatives. The organization required a modern data engineering platform capable of handling growing data volumes while ensuring trusted, analytics-ready datasets.
Our Solution
Databriva designed and implemented a cloud-native data engineering platform based on the Medallion Architecture, enabling a structured and scalable approach to data processing. The solution leveraged Azure Data Lake Storage Gen2 as the centralized data repository, Azure Data Factory for orchestration, and Azure Databricks for distributed data processing. Bronze Layer: Raw data from ERP, CRM, MES, IoT devices, APIs, and flat files was ingested into Azure Data Lake with minimal transformation, preserving historical records and ensuring complete data lineage. Silver Layer: Data was cleansed, standardized, validated, and enriched using Databricks notebooks. Duplicate records were removed, schema inconsistencies were resolved, and business rules were applied to produce high-quality datasets. Gold Layer: Business-ready data models were created to support enterprise reporting, executive dashboards, KPI monitoring, and machine learning workloads. Optimized star schemas and aggregated datasets enabled high-performance analytics in Power BI. The platform incorporated automated pipeline monitoring, metadata-driven processing, incremental data loading, data quality validation, and role-based access control to ensure reliability, security, and scalability.
Results
The Medallion Architecture established a robust foundation for enterprise data engineering by transforming fragmented data into trusted, analytics-ready assets. Automated pipelines reduced manual data processing by more than 85%, while improving data quality, consistency, and governance across the organization. Report refresh times decreased significantly, enabling near real-time business insights for operational and executive reporting. The scalable architecture supported future AI, predictive analytics, and self-service BI initiatives while reducing maintenance overhead and simplifying data platform management.
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