Unifying fragmented data estates into a Databricks Lakehouse saved $4.1M.
Pythian deployed a unified Lakehouse architecture to replace fragmented legacy infrastructure.
The global logistics enterprise faced severe operational gridlock from a fragmented ecosystem of a 12-year-old Teradata warehouse, siloed Oracle databases, and aging Hadoop clusters. Pythian migrated these disconnected assets into a unified, cloud-native Databricks Lakehouse platform with centralized governance. By eliminating complex manual hardware patching, the modern architecture empowered internal teams to build predictive supply chain models instead of managing failing legacy infrastructure. Transitioning to this automated platform allowed the enterprise to minimize operational overhead, secure real-time shipment visibility, and drastically reduce overall costs.
The migration to Databricks completely eliminated our on-premises hardware bottlenecks and structural siloes. Our data teams now spend their time building production AI models for supply chain optimization instead of fighting complex, legacy infrastructure failures."
Director of Data Engineering
Global Logistics Enterprise
4.1M
Annual operational savings
38%
Platform cost reduction
5x
Faster regulatory reporting
Unlock enterprise scale with Databricks modernization.
Global logistics enterprise faced processing delays from legacy multi-vendor databases.
Pythian re-architected the fragile ecosystem into a high-concurrency Databricks Lakehouse platform to accelerate regulatory reporting.
Pythian completely transformed our data infrastructure, migrating us to a unified Databricks Lakehouse platform that took our regulatory reporting timelines from days down to minutes while giving us the scaling power we needed to deploy real-time predictive models."
Director of Data Engineering
Global Logistics Enterprise
Legacy multi-vendor hardware required constant engineering patchwork
The engineering department spent significant development hours patching a legacy Teradata warehouse and fixing cross-platform synchronization failures across legacy Hadoop clusters.
Isolated databases obscured regulatory compliance audits
Fragmented tracking information trapped inside siloed Oracle databases and disconnected legacy environments created major validation gaps that complicated international auditing protocols.
Fragmented database environments bottlenecked peak fleet reporting
Processing petabytes of transactional global shipping data across separate, unoptimized legacy database environments caused extreme query latency during peak fleet operational hours.
Disjointed data architecture stalled predictive route modeling
The disjointed data architecture lacked the high-compute scaling elasticity and unified cataloging needed to train predictive routing algorithms on unstructured streaming data.
Migrated legacy environments into a unified cloud lakehouse.
Pythian engineered a wholesale migration of 2.4 PB of data from the Teradata warehouse, siloed Oracle databases, and unsupported Hadoop infrastructure into a centralized Azure Databricks Lakehouse platform, leveraging automated code conversion to ensure zero data loss.
Implemented automated engineering frameworks for batch processing.
The team replaced complex manual interventions by deploying automated data engineering pipelines with Delta Live Tables and Apache Airflow. Powered by the high-performance Databricks Photon engine, these workflows streamlined high-concurrency batch workloads, shrinking reporting windows from 72 hours to under 45 minutes.
Secured end-to-end compliance via Unity Catalog.
Pythian unified the company’s fragmented data footprint under a centralized governance layer using Unity Catalog. This achieved 100% governance coverage with automated data lineage tracking, entirely eliminating auditing visibility gaps across all global shipping and transactional datasets.
Provided high-compute elasticity for predictive models.
The new architecture delivered the elastic scaling, structured data layers, and MLOps infrastructure via MLflow required by internal teams to build, train, and rapidly deploy three production machine learning models for predictive supply chain, fraud detection, and route optimization.
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