IoT Data Platform
We are witnessing the instrumentation of every available surface in the material world—streets, cars, factories, power grids, ice caps, satellites, clothing, phones, microwaves, milk containers, planets, human bodies. Industry experts estimate that there will be more than 30 billion connected IoT devices by 2020. These devices will generate more data than we have ever experienced. This data is streaming in real time and will force companies to determine which IoT Data Platform architecture will be resilient, scalable, and extensible enough to handle these new workloads.
The purpose of IoT projects is to gather data from sensors or devices in order to gain real-time insights, accelerate decision-making, perform automated tasks, and create value by enabling organizations to become data-driven.
The Distributed IoT Platform
A conceptual model of the distributed IoT platform shows four major functional areas:
- IoT Communication Services: The necessary services that provide device connectivity, message/event queuing and transportation services across Wi-Fi, cellular and fixed connections.
- IoT Security Services: A set of security services that provide encryption and authentication services to ensure that devices (and software on the devices) are secure and tamper-proof; also usually required are additional services that provide security to the communication services.
- IoT Device Management Services: Services that support devices’ provisioning and lifecycle management.
- IoT Data Platform Services: Key set of data services that support collection, aggregation, storage, visualization and analytics of the sensor data.
Modern IoT Data Platform
The new IoT workloads with more data points, more data sources, more monitoring, more controls demand a paradigmatic shift in how we approach building systems that can support these unique characteristics. There is the need for a modern IoT Data Platform which provides a comprehensive set of tools and services that are optimized for IoT data.
We have many customers using InfluxData as an integral part of their IoT architecture, using our Modern Time Series Platform as their IoT Data Platform.
Some Featured IoT Data Platform Use Cases are listed below, or browse all our InfluxData customer testimonials.
tado uses InfluxCloud to provide analytics from its hundreds of thousands of sensors units across the globe. They use this data to power their smartphone apps helping customers understand energy usage and generate energy savings. read more
Spiio chose InfluxData to be its IoT Data Platform. Spiio uses sensors to understand plant performance for optimal green wall growth and maintenance from data. InfluxData provided the scalability and ease of use needed for fast deployment. read more
CREE uses InfluxData as part of its SmartCast Technology platform which collects log data from the LED light fixtures providing real-time IoT monitoring, better insight into energy usage and predictive maintenance. read more
The Modern IoT Data Platform needs to support the unique characteristics of IoT Data:
IoT Data is Time Data: IoT is synonymous with time-stamped data, or time series data since the purpose of any sensor is to measure change over time. To gain insight and act, systems need to evaluate and analyze the data based on timeframes and ranges. The Modern IoT Data Platform needs to be optimized for time series data.
IoT Data is Streaming Data: IoT is synonymous with streaming data. Data is created by a myriad of sensors, and each sensor emits a relentless stream of data. The Modern IoT Data Platform needs to be optimized for streaming data and has actionable analytics to find the signal from all the noise.
IoT Data is Real-Time: Sensors capture and emit data in real time. Greater business value is derived if the data can also be interpreted and acted upon in real time. The Modern IoT Data Platform is designed to handle real-time ingestion and real-time streaming analytics.
InfluxData – The Modern IoT Data Platform
InfluxData delivers a Modern Time Series Platform built from the ground up to support organizations that are looking at building solutions to take advantage of IoT data. Specifically, InfluxData provides the following services:
Data Storage Services: At the heart of the InfluxData offering is InfluxDB, an open source Time Series Database that supports high write loads, large data set storage, and conserves space through downsampling, automatically expiring and deleting unwanted data as well as backup and restore. InfluxDB also makes it easy to analyze data by providing an easy-to-use SQL-like query language. According to DB-Engines, InfluxDB is the leading Time Series Database.
Data Aggregation Services: InfluxData provides a comprehensive set of tools and services to get metrics and events data from sensors, devices, systems, and machines. InfluxData’s collection services are built from the open source Telegraf project. In addition, InfluxData provides services to normalize, correlate, and aggregate this data by using services from the open source Kapacitor project.
Streaming Analytics Services: InfluxData uses the services from the open source project Kapacitor as a native data processing engine. It can process both streaming data or batch data from the data store. Kapacitor lets you plug in your own custom logic, User-Defined Functions (UDFs), or machine learning libraries, to process the data and create alerts with dynamic thresholds, perform pattern matching, compute statistical anomalies, etc. that can then trigger UDFs to form the basis of your IoT control plane.
Visualization Services: InfluxData allows for the graphical real-time visualization of data with the open source project Chronograf, and performs ad hoc exploration of your data. Chronograf includes support for templates and a library of intelligent, pre-configured dashboards for common data sets.