Enabled a large multinational frozen food company to modernize its enterprise analytics foundation by moving over 800 data pipelines from Azure Synapse to a Databricks Lakehouse. The new hybrid medallion and data mesh architecture improved data agility, strengthened governance, accelerated cross-domain analytics, and achieved a 51% reduction in platform costs, all done without disrupting existing reporting or business operations.
The organization worked across 7+ distinct business domains, each with increasing needs for analytics and reporting. Its legacy Azure Synapse setup had grown to over 800 data pipelines making the platform hard to scale, costly to run, and slow to meet new business needs.
Data sharing between areas was limited, governance controls were uneven, and analytics teams spent too much time managing pipelines instead of providing insights. As data volumes and complexity grew, leadership required a more flexible, scalable, and cost-effective analytics foundation that could support decentralized ownership while still maintaining enterprise governance. Blackstraw partnered with the organization to redesign and modernize its analytics platform using Databricks, enabling true data democratization at enterprise scale.
Hybrid Medallion + Data Mesh Architecture: Designed and implemented a hybrid architecture that combined medallion layering (bronze, silver, gold) with data mesh principles, empowering individual business domains while maintaining centralized governance and standards.
Large-Scale Pipeline Modernization: Migrated 800+ data pipelines from Azure Synapse to Databricks using Databricks DLT and Workflows, improving reliability, scalability, and operational simplicity.
Custom Data Engineering Frameworks: Built reusable transformation and aggregation frameworks using PySpark and SQL notebooks to standardize data processing across domains and accelerate analytics development.
Enterprise Governance and Security: Integrated Unity Catalog to enforce centralized data governance, including row-level and column-level access controls, data lineage, and encryption—ensuring secure, compliant data access across teams.
End-to-End Analytics Enablement: Connected the Databricks Lakehouse to Power BI, enabling faster, self-service analytics and reporting for business users across multiple domains.
Improved Analytics Agility: Enabled faster data onboarding, transformation, and insight generation across business units by simplifying pipeline management and reducing engineering overhead.
Scalable Data Sharing Across Domains: Supported secure, governed data sharing between 7+ business domains while preserving domain ownership and accountability.
Stronger Governance and Control: Delivered consistent access controls, lineage, and security policies across the enterprise data landscape.
Cost-Efficient Platform Modernization: Achieved a 51% reduction in overall platform costs by leveraging Databricks serverless compute and optimized pipeline execution.
Enterprise-Ready Data Foundation: Established a scalable, future-ready analytics platform capable of supporting advanced analytics, AI, and cross-domain decision-making.
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