Building a Unified Lakehouse: Databricks Migration for a Data-Fragmented Enterprise
How a multi-regional logistics operator unified 8 fragmented data systems into a single Databricks Lakehouse, cutting reporting latency from 48 hours to near real-time.
48hrs → seconds
Data pipeline latency for shipment metrics
Meet our client
Client
A leading multi-regional logistics and supply chain operator
Industry
Transportation & Logistics
Market
Europe & Africa
Technologies
Client's Challenge
The client struggled with highly fragmented operational data scattered across legacy relational databases, on-premise transactional systems, and disconnected third-party cloud applications. This separation made it impossible to achieve real-time visibility into supply chain bottlenecks, fleet utilization, or predictive delivery timelines.
To train operational machine learning models, engineers had to spend days manually querying, cleaning, and moving raw files, leading to stale reporting and delayed business decisions.
Our Solution
CipherSense AI architected and implemented a modern, unified Databricks Lakehouse platform on AWS to act as the single source of truth for all business intelligence and machine learning workloads.
- 01
Automated Infrastructure-as-Code
Designed and deployed the entire Databricks environment utilizing Terraform to ensure repeatable, secure, and compliant cloud deployment across regions.
- 02
Medallion Data Pipeline Design
Built real-time and batch ingestion pipelines (using Spark and Delta Live Tables) structured into a standardized medallion architecture. Bronze: raw ingestion of structured vehicle IoT feeds, ERP transactional data, and unstructured logistics manifests. Silver: cleaned, conformed, and enriched tables with standardized schemas and unified time zones. Gold: aggregated, business-level tables optimized for rapid downstream BI querying and ML feature store ingestion.
- 03
Unified Governance & Quality Controls
Configured Unity Catalog to enforce granular, row- and column-level access controls while embedding automated data quality validation rules into the pipeline.
Client's Benefits
True Single Source of Truth
Replaced 8 fragmented legacy data repositories with a single, highly performant Databricks Lakehouse platform.
From Days to Seconds
Reduced data pipeline latency for critical shipment metrics from a 48-hour batch cycle to near real-time updates.
90% Faster ML Preparation
Cut the time data scientists spent locating and preparing datasets for predictive maintenance and route-optimization models from weeks to minutes.
“We stopped arguing about whose numbers were right and started building on top of numbers everyone trusted.”
Head of Data, client logistics operator
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