CortexFlow

The main reason CortexFlow exists is to overcome the crucial limitations of fragmented IoT frameworks. We focus on unifying batch and real-time data processing in a flexible, Python-powered environment for seamless operations. By enabling large-scale Exploratory Data Analysis (EDA) without down-sampling, we help uncover insights in vast datasets. With Kubernetes and Docker integration and a microkernel written in Rust, we aim to scale containerized microservices for managing distributed IoT devices. Machine learning models can be deployed across thousands of distributed and personalized clusters for fast, fault-tolerant decisions.

CortexFlow combines microservices, data mesh, and data fabric to power scalable IoT simulation and big data analytics. Python handles data processing, while Rust ensures performance with a microkernel. Kubernetes and Docker orchestrate containerized services, offering flexibility and scalability across distributed environments. The data mesh decentralizes data management, empowering domain teams, while the data fabric integrates access across diverse systems. The stack includes IoT BD platform for custom big data processing and supports large-scale machine learning and real-time analytics, for an efficient IoT ecosystem management.