
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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The HiveMQ Platform provides a scalable, reliable data backbone with an event-driven MQTT architecture. Here are a few highlights:
1. MQTT Broker: At the heart of the HiveMQ platform is a fully MQTT-compliant broker purpose-built for fast, reliable, bi-directional data movement between IoT devices and enterprise systems.
2. Edge Data Integration: HiveMQ Edge seamlessly integrates edge data by converting industrial protocols into standardized MQTT, enabling an interoperable IIoT infrastructure.
3. IoT Streaming Governance: Data Hub transforms data in flight, passing only the most relevant, contextualized data to cloud and enterprise systems.
4. UNS & IT/OT convergence Enabler: Commonly used as the backbone for Unified Namespace architectures and seamlessly connects OT devices with IT systems for full visibility and interoperability.
5. Distributed Data Intelligence: HiveMQ Pulse unifies and contextualizes data across the enterprise for smarter decisions exactly where they matter most.
6. Maximum Interoperability: Runs anywhere on-premises or in public or private clouds. Efficiently connects to streaming applications, databases and data lakes with a Java SDK to build your own
7. Scalability to Support Growth: Elastic scaling with automatic data balancing and smart message distribution. Proven benchmark of up to 200M active clients with 1.8B messages/hour
8. Business Critical Reliability: Zero message loss with persistence to disk and offline queuing. No single point of failure due to masterless cluster architecture and zero downtime upgrades
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Apollo Autonomous Vehicle Platform
A combination of sensors, including LiDAR, cameras, and radar, gather data from the vehicle's surroundings. By employing sensor fusion technology, perception algorithms are capable of identifying, locating, measuring the speed, and determining the orientation of various objects on the road in real time. This advanced autonomous perception system is supported by Baidu's extensive big data infrastructure and deep learning capabilities, along with a rich repository of labeled real-world driving data. The robust deep-learning platform, complemented by GPU clusters, enhances processing power. Additionally, the simulation environment enables virtual driving across millions of kilometers each day, leveraging diverse real-world traffic and autonomous driving data. Through this simulation service, partners can access an extensive array of autonomous driving scenarios, allowing for rapid testing, validation, and optimization of models in a manner that prioritizes both safety and efficiency, ultimately fostering advancements in autonomous vehicle technology.
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Mobileye
Mobileye offers a wide range of advanced driver-assistance systems (ADAS), extending to autonomous vehicles for public transportation and goods delivery, as well as consumer automotive applications. By managing the entire process from silicon production to the implementation of self-driving technology internally, Mobileye harnesses multiple efficiencies and synergies, enabling large-scale deployment of autonomous vehicles. Since its inception, the company has focused on developing both hardware and software in-house, which has led to the creation of highly effective technological stacks that excel in cost-performance efficiency. Every product from Mobileye is designed with safety as a core principle, supported by a comprehensive strategy aimed at making this cutting-edge technology accessible to the general public. This commitment to innovation and safety ensures that Mobileye is well-positioned to lead the future of autonomous driving.
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