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Ratings and Reviews 0 Ratings
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RaimaDBRaimaDB is an embedded time series database designed specifically for Edge and IoT devices, capable of operating entirely in-memory. This powerful and lightweight relational database management system (RDBMS) is not only secure but has also been validated by over 20,000 developers globally, with deployments exceeding 25 million instances. It excels in high-performance environments and is tailored for critical applications across various sectors, particularly in edge computing and IoT. Its efficient architecture makes it particularly suitable for systems with limited resources, offering both in-memory and persistent storage capabilities. RaimaDB supports versatile data modeling, accommodating traditional relational approaches alongside direct relationships via network model sets. The database guarantees data integrity with ACID-compliant transactions and employs a variety of advanced indexing techniques, including B+Tree, Hash Table, R-Tree, and AVL-Tree, to enhance data accessibility and reliability. Furthermore, it is designed to handle real-time processing demands, featuring multi-version concurrency control (MVCC) and snapshot isolation, which collectively position it as a dependable choice for applications where both speed and stability are essential. This combination of features makes RaimaDB an invaluable asset for developers looking to optimize performance in their applications.
What is Intelense KEN AI?
Historically, the management of properties and assets has posed significant challenges and risks, often characterized by complex processes. By utilizing pre-existing cameras, sensors, and devices, we can derive insightful analytics that significantly improve operational efficiency. Our state-of-the-art AI technology analyzes this data to identify activities concerning safety, health, and compliance, which allows for the automation of various tasks. We have developed an extensive range of solutions driven by KEN AI™, specifically designed to enhance health, safety, and emergency services. These applications are fully compatible with IoT and 5G technologies and come equipped with open APIs for easy integration. The scope of AI Analytics encompasses multiple areas, including traffic management, crowd control, airport operations, and emergency response systems. Our AI-enriched video technology is particularly focused on protecting your most critical asset—your workforce. KEN AI continuously monitors and addresses safety risks in real time during operations. Additionally, our APIs are capable of identifying anomalies both in recorded incidents and within the IoT sensors, providing a thorough approach to safety management. The integration of these cutting-edge solutions is set to transform the future of asset management significantly. Ultimately, our mission is to create a safer and more efficient environment for all stakeholders involved.
What is SQLstream?
In the realm of IoT stream processing and analytics, SQLstream has been recognized as the leading solution by ABI Research. Our technology, utilized by major corporations such as Verizon, Walmart, Cisco, and Amazon, facilitates applications across various environments, including on-premises, cloud, and edge computing.
SQLstream's capabilities allow for the generation of urgent alerts, dynamic dashboards, and immediate responses with latency measured in sub-milliseconds. This enables smart cities to efficiently redirect emergency services and optimize traffic signal operations based on current conditions. Additionally, security frameworks can swiftly identify and neutralize cyber threats, ensuring safety and integrity. Furthermore, AI and machine learning models, developed using streaming sensor inputs, are capable of forecasting potential equipment malfunctions.
Due to SQLstream's remarkable speed — accommodating up to 13 million rows per second for each CPU core — organizations have significantly minimized their operational costs and physical infrastructure. Our advanced in-memory processing fosters capabilities at the edge that would typically be unfeasible.
Users can acquire, prepare, analyze, and take action on data across various formats and sources seamlessly. With StreamLab, our user-friendly, low-code development environment, creating data pipelines becomes a task that can be accomplished in minutes rather than months. Instant script editing and real-time result visualization without the need for compilation streamline the development process further. Deployment is made easier with robust support for Kubernetes, simplifying integration into existing workflows.
The installation process is user-friendly and compatible with a variety of platforms, including Docker, AWS, Azure, Linux, VMWare, and others, ensuring flexibility for diverse operational needs.
Integrations Supported
Amazon Kinesis
Apache Kafka
RabbitMQ
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Intelense
Date Founded
2018
Company Location
Canada
Company Website
www.intelense.com/applications
Company Facts
Organization Name
Guavus, a Thales company
Date Founded
2006
Company Location
United States
Company Website
sqlstream.com
Categories and Features
Artificial Intelligence
Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)
IoT
Application Development
Big Data Analytics
Configuration Management
Connectivity Management
Data Collection
Data Management
Device Management
Performance Management
Prototyping
Visualization
Categories and Features
Streaming Analytics
Data Enrichment
Data Wrangling / Data Prep
Multiple Data Source Support
Process Automation
Real-time Analysis / Reporting
Visualization Dashboards