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
Operating a siloed Teradata warehouse alongside legacy Hadoop hardware required heavy maintenance, draining engineering time and increasing physical datacenter costs.
Isolated databases obscured regulatory compliance audits
Data scattered across isolated relational databases made global compliance and automated tracking highly complex and prone to validation errors.
Fragmented database environments bottlenecked reporting
Running heavy analytical queries across fragmented, aging environments led to severe data processing backlogs and delayed time-sensitive fleet insights.
Disjointed data architecture stalled predictive route modeling
The disconnected environment lacked the modern data foundation and computational power necessary to efficiently train and run predictive machine learning models.
Unified multi-vendor data estates lowered infrastructure overhead.
Pythian migrated 2.4 PB of data from legacy Teradata, Oracle, and Hadoop systems into a centralized Azure Databricks Lakehouse. Automated code conversion accelerated the timeline and protected data integrity, allowing the enterprise to decommission expensive on-premises hardware and slash platform costs by 38%.
Automated batch processing frameworks accelerated operational reporting.
The team replaced fragile manual workflows with automated engineering pipelines built on Delta Live Tables and Apache Airflow. Powered by the high-performance Databricks Photon engine, this optimization eliminated peak-hour processing delays, shrinking reporting windows from 72 hours to under 45 minutes.
Standardized end-to-end data controls de-risked compliance audits.
Pythian consolidated the company’s fragmented footprint under a single governance layer using Unity Catalog. Delivering 100% governance coverage with automated data lineage and strict access security entirely eliminated auditing visibility gaps, allowing the enterprise to confidently pass international regulatory reviews.
Unlocked high-compute scaling to drive predictive supply chain intelligence.
The new architecture established the elastic scaling and clean MLOps infrastructure via MLflow needed to operationalize advanced analytics. The stable foundation shifted internal teams from reactive maintenance to innovation, enabling them to quickly deploy three production machine learning models for route optimization.
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