Logistics and transportation applications are dealing with the rapid growth of data as sensors, people and and systems increasingly send more and more data for consideration. Adding the GridGain® in-memory computing platform to a logistics or transportation application enables massive scalability to deal with the growth in data as well as a 1,000x improvement in processing speeds as data moves from disk to RAM. GridGain enables extremely high performance for organizations dealing with massive amounts of location-based data.
GridGain provides in-memory speed and massive scalability for transactional, analytical and hybrid transactional/analytical processing applications. GridGain is solid and proven technology that delivers predictable latency, flexible scaling, configurable data consistency and reliable uptime. GridGain can run on commodity hardware, virtual machines or cloud providers. GridGain is built on the open source Apache® Ignite™ project code base.
Logistics and Transportation Clients
MercuryGate International delivers collaborative, global Transportation Management Systems (TMS) for shippers, carriers, brokers, freight forwarders and third party logistics providers. Read the MercuryGate case study to learn how they utilize GridGain in-memory computing technology.
They provide data so businesses can market to their best customers and they enable the sending of parcels and packages across the globe. It enables companies to derive enhanced business insights from the visualization, analysis and modeling of location and Geographic Information Systems (GIS) data.
Nederlandse Spoorwegen is the principal passenger railway operator in the Netherlands. It has developed into a comprehensive service provider, enabling its customers to blend social, business and recreational goals seamlessly.
Logistics and Transportation Use Cases for GridGain Technology
Event Driven Architectures
Prediction Modeling for Inventory Logistics
Parcel Volume Predictions
Parcel Health Monitoring
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Join Nikita Ivanov, CTO of GridGain Systems and member of the Project Committee for Apache Ignite, to learn how to boost performance 1,000x and scale to over 1 billion transactions per second with in-memory storage of hundreds of TBs of data for your SQL-based applications.
Nikita will show how Apache Ignite handles auto-loading of SQL schema and data, SQL indexes, compound indexes support, and various forms of SQL queries including distributed SQL joins, joins across caches, predicate-based queries, queries over sliding windows for streaming data, and more.