Datansh helped a global chipset manufacturer replace a slow, manual-heavy Synapse transformation layer with a Databricks-based pipeline architecture that cut cost, reduced manual effort, and enabled near real-time analytics.
-50% ETL operating cost through Databricks cluster optimization and removal of Synapse overhead.
60–70% faster load and transform cycles after migrating the ETL architecture.
-80% manual schema-handling effort using PySpark, DLT, and metadata-driven automation.
The existing Synapse-based transformation layer was expensive, slow, and required frequent manual intervention for schema updates and transformation maintenance, which delayed analytics delivery and created unnecessary operational overhead.
Datansh re-architected the ETL layer in Databricks using Delta Live Tables, PySpark notebooks, JSON-driven metadata definitions, and a layered Bronze-Silver-Gold model that could support more automated, scalable, and reliable downstream reporting.
More work in manufacturing and adjacent operational environments.
Datansh helped a U.S.-based light manufacturer transform long, unstructured CRM conversation histories into predictive next-action intelligence, giving sales reps and managers faster clarity on how to move enterprise deals forward.
Datansh helped a multinational tyre manufacturing organization move from disparate, semi-structured data flows to a centralized batch-and-streaming platform that treated data as a product and improved enterprise reporting reliability.
Datansh helped a U.S.-based multinational manufacturer replace static, number-heavy ERP reporting with a secure Azure-based analytics foundation that unified legacy ERP data, connected additional enterprise sources, and turned reporting into interactive business visibility.