To handle real-time data processing on limited hardware, use event-driven architecture and lightweight stream processing frameworks like Kafka to manage data efficiently. Prioritize and filter data at the source, applying in-memory tools like Redis for quick access to high-priority data. Adopt microservices or edge computing to distribute workloads, preventing hardware overload. Employ a hybrid Lambda architecture to process essential data in real time while batching non-urgent tasks. Tools like Prometheus help monitor performance and identify bottlenecks, ensuring low-latency, resource-efficient processing despite hardware constraints.

This question does not mean much without specifying whether are we talking about "hard real-time" or "soft real-time". Contrary to popular belief, "real-time" does not mean "blazingly fast" and often has nothing to do with efficiency; it is mostly about results of missing deadlines (well, normally they are in an order of milliseconds, but it is not a rule). Returning to the topic, the design of hard/soft real-time systems is enormously difficult independent of hardware constraints, and there is no general recipe for how it should be done. Probably the best bet is to find some real-time operating system capable of running on the given hardware and design the system on top of it.

I use a combination of device drivers (interrupt level and system state), driver-assist processes, and normal processes to triage the needed processing into immediate responses(< 100us); fast responses (<500us), and slower (I do not claim originality, David Cutler's 1976 paper "The Nucleus of a Realtime Operating System" is the earliest source that I have identified. The above hierarchy works well. Isolating actual realtime low-latency operations within device drivers keeps them isolated, improving system integrity. Using a driver also prevents conflicts when different software components are contending for device access. Drivers also isolate changes in hardware and connectivity. Using this approach has avoided many common problems.

To handle real-time data processing on a system with limited hardware resources, there are various strategies that can optimize memory, CPU, and latency usage. Using algorithms optimized for time and space complexity is essential. Algorithms that reduce the need for memory and minimize the number of operations can make a big difference. To make the most of the available cores, the system can divide operations into parallel threads (if the hardware supports it) or use priority queues for processes that require fewer resources. In this way, efficient processing can be maintained even in real-time contexts with hardware constraints, balancing available resources and optimizing each phase of the process.

1. Investigate Real-time data type and critical resources. a) Memory Driven: Optimized Caching, Quick release for next processing b) IO Driven : Optimized read/write, in memory processing c) CPU intense: affinity, priority and cycle optimization 2) Optimized pub/sub, message queue system 3) Process the data closer to the source of the data, bring controller/processor to that device.