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Driving Freight Transportation into the Future: A Case Study on J.B. Hunt
Technology Category
- Functional Applications - Transportation Management Systems (TMS)
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Buildings
- Transportation
Applicable Functions
- Logistics & Transportation
Use Cases
- Autonomous Transport Systems
- Transportation Simulation
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
The Challenge
J.B. Hunt, a leading American transportation and logistics company, was facing significant challenges in their quest to become the most efficient freight transportation network in North America. Their primary focus was on connecting carriers with their ideal shipper, considering factors such as price, weight, and location. However, their legacy architecture, lack of AI capabilities, and inability to securely handle big data were causing significant roadblocks. Their data was locked in legacy enterprise data warehouse (EDW) platforms, and their systems struggled to process and store the massive data generated by hundreds of thousands of equipment pieces. They also lacked the necessary levels of data security and the ability to support data streams generated by IoT sensors on their trucks and carriages. The company knew it was time for a change.
About The Customer
J.B. Hunt Transport Services, Inc. is an American transportation and logistics company based in Lowell, Arkansas. Founded over 60 years ago by Johnnie Bryan Hunt and Johnelle Hunt, it has grown to be a Fortune 500 company and one of the largest trucking firms in the U.S. The company's mission is to build North America’s most efficient transportation network, focusing on streamlining freight logistics and providing the best carrier experience possible. They aim to connect carriers with their ideal shipper, taking into consideration details such as price, weight, and location.
The Solution
To modernize their data infrastructure, J.B. Hunt moved to the Databricks Lakehouse Platform, which unified data engineering and data science functions on one open and scalable platform. With Databricks Delta Lake, they were able to put all their data in one place for easy access across the organization and ensure the performance and reliability of streaming data pipelines at any scale. They also integrated Immuta with Databricks to provide cloud data access control, giving them a level of data security that wasn’t possible with their legacy EDW. J.B. Hunt had over 200 users that needed access to sensitive data and required full auditing, anonymization, and time-based access controls. Implementing Immuta on Databricks was straightforward and led to massive cost savings. The solution required flexibility to rapidly create and update data access rules.
Operational Impact
Quantitative Benefit
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