Swedbank, the largest banking group in Sweden and the third largest in the Nordics, was faced with the challenge of increasing operational costs due to its on-premise data stack. The existing solution was not only expensive to maintain but also lacked the flexibility to adapt to new business requirements. It replicated data across siloed platforms, had limited capabilities to separate storage from compute, and lacked support for deep learning and AI capabilities. As part of its digital transformation journey, Swedbank sought to unify disparate data sources into a single data lake, migrate analytical capabilities to the cloud, uncover deeper insights at scale, and accelerate time to market of data use cases. The bank's strategic vision was to build a resilient, scalable infrastructure to enable the widespread availability of advanced analytics while streamlining the analytics process to achieve operational efficiency.

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.