
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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Compute Engine (IaaS), a platform from Google that allows organizations to create and manage cloud-based virtual machines, is an infrastructure as a services (IaaS).
Computing infrastructure in predefined sizes or custom machine shapes to accelerate cloud transformation. General purpose machines (E2, N1,N2,N2D) offer a good compromise between price and performance. Compute optimized machines (C2) offer high-end performance vCPUs for compute-intensive workloads. Memory optimized (M2) systems offer the highest amount of memory and are ideal for in-memory database applications. Accelerator optimized machines (A2) are based on A100 GPUs, and are designed for high-demanding applications. Integrate Compute services with other Google Cloud Services, such as AI/ML or data analytics. Reservations can help you ensure that your applications will have the capacity needed as they scale. You can save money by running Compute using the sustained-use discount, and you can even save more when you use the committed-use discount.
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FreeRTOS
Developed in collaboration with top chip manufacturers over a span of 15 years, FreeRTOS is now downloaded approximately every 170 seconds and stands as a top-tier real-time operating system (RTOS) tailored for microcontrollers and small microprocessors. Available at no cost under the MIT open source license, FreeRTOS encompasses a kernel along with an expanding collection of IoT libraries that cater to various industries. Prioritizing reliability and user-friendliness, FreeRTOS is renowned for its proven durability, minimal footprint, and extensive device compatibility, making it the go-to standard for microcontroller and small microprocessor applications among leading global enterprises. With a wealth of pre-configured demos and IoT reference integrations readily available, users can easily set up their projects without any hassle. This streamlined process allows for rapid downloading, compiling, and quicker market entry. Furthermore, the ecosystem of partners offers a diverse range of options, including both community-driven contributions and professional support, ensuring that users have access to the resources they need for success. As technology continues to evolve, FreeRTOS remains committed to adapting and enhancing its offerings to meet the ever-changing demands of the industry.
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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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