A large enterprise struggled with fragmented data spread across multiple operational systems, making it difficult to deliver consistent reporting, trusted analytics, and timely business insights. Blackstraw implemented a cloud-native Enterprise Data Platform on Google Cloud using Databricks and BigQuery, creating a centralized, governed foundation for enterprise data. The platform improved data accessibility, strengthened governance, and enabled analytics and AI initiatives at scale.
The organization required a modern data platform capable of securely storing and processing growing volumes of structured and unstructured data from multiple sources. Beyond centralizing data, the platform needed to support self-service analytics, advanced machine learning, and enterprise-wide governance while remaining scalable for future business and technology needs. Blackstraw designed a multi-zone architecture that standardizes data ingestion, transformation, governance, and consumption, providing trusted data products for business users, analysts, and data scientists.
Multi-Zone Cloud Data Architecture: Designed a cloud-native data platform on Google Cloud Storage (GCS) with dedicated Landing, Bronze, Silver, and Gold zones to support secure, scalable data processing and environment isolation.
Governed Data Ingestion: Ingested data from multiple operational systems into the Bronze layer, applying data quality rules and cataloging assets through Unity Catalog to establish trusted data foundations.
Standardized Data Transformation: Leveraged Databricks Workflows to cleanse, standardize, enrich, and model enterprise data into reusable fact and dimension tables within the Silver layer.
Enterprise Data Products: Delivered curated business-ready datasets in the Gold layer, enabling reporting, analytics, and downstream business applications.
Unified Data Governance: Implemented Unity Catalog with row-level and column-level security controls to provide secure, role-based access to enterprise data.
35% faster data onboarding: Standardized ingestion and transformation patterns accelerated the addition of new data sources and business domains.
25% reduction in platform support effort: Reusable pipelines, centralized orchestration, and automated monitoring reduced operational overhead.
Improved trust in enterprise data: Centralized lineage, cataloging, data-quality controls, and fine-grained access created more reliable datasets for reporting and analytics.
Faster access to analytics-ready data: Curated Gold-layer data products reduced the time business and analytics teams spent preparing data for consumption.
Scalable foundation for AI and advanced analytics: Established a governed platform that could support new ML, GenAI, and enterprise analytics use cases without major re-architecture.
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