Empowering the Disconnected Edge: Shifting Far Left with Predictive Analytics for Naval Ships

A Navy ship is essentially a large edge node with unique complexities…let me explain. While you may not think of a ship as an edge node due to its size, it does share similar use cases that are seen on typical edge-based deployments. Sensor data is collected and needs to be aggregated and disseminated to multiple environments including shore and cloud sites. Sharing data in a denied, disrupted, intermittent, and limited (DDIL) environment presents a significant challenge.

A Navy ship, when deployed, can also spend 6+ months out at sea before returning to port. For predictive analytics at the disconnected edge, a key consideration is how to manage software updates, including updates to the analytical models themselves.

In this session, we will explore how Confluent (Kafka) and Databricks are solving the problems with predictive analytics at the edge and bridging the operational and analytical domains. We will demonstrate how Cluster Linking can be leveraged with DDIL and smart edge processing by prioritizing topics when bandwidth is restricted. We will use logistics data to develop analytics using Delta Live Tables and mlflow that can be used for predicting failures in equipment on the ship. And finally, how the analytics can be deployed to the ship, while at sea, for real-time reporting using Apache Flink.

Attendees will leave with understanding of the complexities of edge-based analytics and a blueprint for setting up a pipeline to overcome those challenges in real-world applications.