Evolving from the world of Machine to Machine (M2M), the Internet of Things (IoT) blends data from multiple devices and enterprise systems with rich, real time analytics to create new levels of operational insight, efficiency and opportunity. Telematics and mobility, retail and intelligent supply chain management, manufacturing automation, mHealth and patient monitoring, the connected home and smart utility grids are all industries and applications that are emerging from IoT advancements.
There are a number of technology components required to deliver the IoT vision. M2M sensors generate huge volumes of data that needs to be ingested, stored and analyzed. The database is at the core of IoT platforms. It can be an enabler -- or a blocker -- to realizing the vision.
Leading organizations are using MongoDB to build IoT applications that:
- Deliver operational insight and lower costs: using in-store sensors and smart trolleys to track shoppers around the store, optimizing the placement of high margin products and reducing risks of theft.
- Unlock new revenue streams: leveraging arrays of new in-vehicle sensors connected to the engine data bus to deliver new services tracking driver behavior and maximizing fuel efficiency.
- Improve customer service and support: running constant diagnostics and real time analytics against production lines or heavy plant equipment to identify performance deviations and schedule preventative maintenance.
- Business Agility: IoT is in its infancy. New use cases driven by the proliferation of sensors across physical assets coupled with emerging standards and new regulatory controls demands a flexible and dynamic data storage architecture.
- Multiple Devices and Data Types: An IoT solution should be able to incorporate rapidly changing, multi-structured data from a variety of sources. For example, location data generated from smartphones can be blended with user preferences and demographics to deliver precisely targeted promotions and offers.
- Data Volume and Velocity: Industry forecasters expect 50 billion sensor-equipped “smart objects” by 2020. Just 100,000 vehicles reporting their location once every 60 seconds generates 144 million data points per day. Traditional data management technologies were never designed to handle this amount or speed of IoT data.
- Real Time Operational Insight: Analyzing, visualizing and responding to sensor output (e.g., real-time supply chains, manufacturing process control) requires powerful tools able to run complex, low latency queries across rapidly changing data sets.
- Enterprise-Grade: From PCI-DSS compliant retail systems to continuous process control systems in manufacturing, IoT applications are increasingly embedded within the operational fabric of the business. They must deliver the scalability, availability and security demanded by any enterprise application.
- Agility: MongoDB’s dynamic schema means that application development and ongoing evolution are straightforward, enabling continuous integration as developers add new features. Idiomatic drivers enable developers to work in their preferred programming languages.
- Data Volume and Velocity: Auto-sharding enables MongoDB to automatically partition and scale rapidly growing data sets across fleets of low cost, commodity servers, with complete application transparency.
- Real-Time Business Discovery: With rich query support, including secondary, geospatial and text search indexes, the Aggregation Framework and native MapReduce, MongoDB can run complex ad-hoc analytics across operational data to deliver real-time insight. Integration with the leading Apache Hadoop distributions and many of the most popular BI (Business Intelligence) and Analytics tools enables seamless reporting.
- Enterprise Assurance: Self-healing clusters, coupled with strong access controls, auditing and encryption ensure IoT data is always available and always secure.
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