10x your Internet of Things analytics projects
Knowi has been selected as IoT analytics platform of the year
Smart Cities IoT Dashboard
IoT Analytics Dashboard Example
Allow event operators to monitor capacity in real-time using a single dashboard to easily identify opportunities to reduce waste. Use anomaly detection to reduce risk and anticipate safety hazards.
IoT Fleet Management Dashboard Example
Monitor the status of your cars, bikes, mopeds, trucks to schedule preventive maintenance and understand operational under different conditions.
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Knowi for IoT Analytics
Native Integration to IoT Data Sources
Knowi natively integrates to all the popular time-series NoSQL data stores enabling instant integration to your IoT data, without moving or manipulating it first. We don't move your data for analytics or require you to install any drives, etc. When we say native integration, we mean native.
Join with Relational Data Sources
Knowi uniquely enables you to create blended datasets on the fly through our innovative query interface. Join as many sources as your want, including REST-APIs, in a single query. All join types are supported. All you need to know if the join key and we take care of the rest.
Embed Analytics in Your IoT Applications
Knowi comes with over 30 visualizations that you can easily embed into your IoT applications. We provide a number of embed options to meet every use case. Brand Knowi dashboards as your own for a continuous user experience for your business teams, customers or partners.
Frequently Asked Questions
Questions and answers about IoT analytics
IoT data is distributed and disparate in nature. This means that to enable real-time IoT data streaming and analytics, you must have an efficient method of data ingestion or integration. The ingestion step usually involves automated data collection across different data sources, aggregating everything into a data warehouse or data lake. Depending on the nature of the data collected, the design of the ingestion process and storage will vary widely.
In data transformation, you merge or join the collected data as needed and run any additional operations to get it into the optimal format.
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