RaimaDB
RaimaDB, an embedded time series database that can be used for Edge and IoT devices, can run in-memory. It is a lightweight, secure, and extremely powerful RDBMS. It has been field tested by more than 20 000 developers around the world and has been deployed in excess of 25 000 000 times.
RaimaDB is a high-performance, cross-platform embedded database optimized for mission-critical applications in industries such as IoT and edge computing. Its lightweight design makes it ideal for resource-constrained environments, supporting both in-memory and persistent storage options. RaimaDB offers flexible data modeling, including traditional relational models and direct relationships through network model sets. With ACID-compliant transactions and advanced indexing methods like B+Tree, Hash Table, R-Tree, and AVL-Tree, it ensures data reliability and efficiency. Built for real-time processing, it incorporates multi-version concurrency control (MVCC) and snapshot isolation, making it a robust solution for applications demanding speed and reliability.
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Dragonfly
Dragonfly serves as a seamless substitute for Redis, offering enhanced performance while reducing costs. It is specifically engineered to harness the capabilities of contemporary cloud infrastructure, catering to the data requirements of today’s applications, thereby liberating developers from the constraints posed by conventional in-memory data solutions. Legacy software cannot fully exploit the advantages of modern cloud technology. With its optimization for cloud environments, Dragonfly achieves an impressive 25 times more throughput and reduces snapshotting latency by 12 times compared to older in-memory data solutions like Redis, making it easier to provide the immediate responses that users demand. The traditional single-threaded architecture of Redis leads to high expenses when scaling workloads. In contrast, Dragonfly is significantly more efficient in both computation and memory usage, potentially reducing infrastructure expenses by up to 80%. Initially, Dragonfly scales vertically, only transitioning to clustering when absolutely necessary at a very high scale, which simplifies the operational framework and enhances system reliability. Consequently, developers can focus more on innovation rather than infrastructure management.
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Zephyr
Ranging from basic embedded environmental sensors and LED wearables to advanced embedded controllers, smartwatches, and IoT wireless applications, this system incorporates configurable architecture-specific stack-overflow protection, kernel object and device driver permission tracking, and thread isolation enhanced by thread-level memory protection across x86, ARC, and ARM architectures, as well as userspace and memory domains. For systems lacking MMU/MPU and those limited by memory capacity, it enables the integration of application-specific code with a tailored kernel to form a monolithic image that can be loaded and run on the hardware of the system. In this setup, both the application and kernel code operate within a unified address space, facilitating efficient resource utilization and performance optimization. This design ensures that even resource-constrained environments can effectively leverage complex applications and functionalities.
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Mbed OS
Arm Mbed OS is an open-source operating system tailored for IoT applications, providing all the essential tools for creating IoT devices. This robust OS is equipped to support smart and connected products built on Arm Cortex-M architecture, offering features such as machine learning, secure connectivity stacks, an RTOS kernel, and drivers for various sensors and I/O devices. Specifically designed for the Internet of Things, Arm Mbed OS integrates capabilities in connectivity, machine learning, networking, and security, complemented by a wealth of software libraries, development boards, tutorials, and practical examples. It fosters collaboration across a vast ecosystem, supporting over 70 partners in silicon, modules, cloud services, and OEMs, thereby enhancing choices for developers. By leveraging the Mbed OS API, developers can maintain clean, portable, and straightforward application code while benefiting from advanced security, communication, and machine learning functionalities. This cohesive solution ultimately streamlines the development process, significantly lowering costs, minimizing time investment, and reducing associated risks. Furthermore, Mbed OS empowers innovation, enabling developers to rapidly prototype and deploy IoT solutions with confidence.
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