This case study will tell you how we partnered with a leading pet health innovator to enhance nutrition technology through a cloud-based system that uses real-time data streaming. See how we used tools like Google Cloud IoT Core and Apache Kafka to build a scalable IoT solution that processes large amounts of telemetry data from wearable sensors and beacons, providing valuable insights into pet health and behavior.
This case study will tell you how we partnered with a leading pet health innovator to enhance nutrition technology through a cloud-based system that uses real-time data streaming. See how we used tools like Google Cloud IoT Core and Apache Kafka to build a scalable IoT solution that processes large amounts of telemetry data from wearable sensors and beacons, providing valuable insights into pet health and behavior. Key Highlights:
The company also works on multiple projects with multiple healthcare professionals on groundbreaking nutrition technology and innovation to create products that lead to fulfilling, long lives. To achieve this, they used technologies like sensors, beacons, software, services, and its own IoT cloud to monitor pet behaviors (sleep, walk, eat, play, etc.) and capture the results. The data collated was used to create nutritional products. But with the volume of data increasing, the company wanted a better way to manage it to facilitate and accelerate innovation. They partnered with us at Cambridge Technology to build a core platform for telemetry, data ingestion, and integration, leveraging Cloud IoT Core on Google Cloud Platform and Apache Kafka®.
We, at Cambridge Technology, architected an event-driven platform designed to ingest high-ingress IoT data built on Apache Kafka at its core. Being a fully managed cloud service, Cloud IoT Core allows us to quickly and securely connect, manage, and ingest data from many devices with sensors that record pet activity. Apache Kafka enabled asynchronous communication between applications and microservices with real-time, event-based streaming and persistent storage of events. The data analysts and scientists also brought the solution to other projects to combine wearables and analytical lab data sets and associated phenotypes. It used the event streaming architecture and streaming engines to process and respond to events in near real-time and build streaming applications and data pipelines instead of waiting for the data from batch processing.
With Apache Kafka at play, the teams got an easier way to scale and deploy analytical models. It acted as a central nervous system for their data, thereby helping them conduct training and deployments faster than ever before Once implemented, this architecture worked as the common ground that tied a broad range of development projects. The team also engaged multiple research counterparts while ensuring that all the applications would share the same event streaming platform as a unified room for data integration. The solution laid the foundation for rapid innovation, quickly by enabling them to develop several new applications faster. The solution was also highly scalable and distributed, and it cut out maintenance complexities and overheads with a seamless solution that worked without downtime. One of the most significant advantages that the company witnessed was that the solution put them on the track for becoming future-ready — courtesy to real-time analytics and visibility. Since the data analysts and scientists could handle data messaging, storage, and streaming within a single platform, they could keep their data secure on the Google Cloud Platform. The platform also enabled real-time reporting to get data reports in near real-time instead of waiting 24 hours as they did before. It allowed them to react faster to changes and make quick decisions based on solid analytics. With a solution like this, the company was ready to meet the changing needs of nutritional research and help accelerate their product innovation.
