From production lines to patrol cars to pacemakers, sensor-enabled objects are unleashing torrents of data previously unimaginable. While new revenue opportunities beckon, sensor data is only useful if you can do something with it.
With MongoDB, you can make sense of sensor data, building faster and less expensive applications that never before were possible.
IoT Is Difficult
Companies Can’t Stay Ahead.
Each new generation of “things” comes with new sensors. New sensors create new data and new functionality requirements. Relational databases make it hard to incorporate new data and iterate on your data model.
Companies Can't Scale.
40 billion sensors generate volumes of data. That’s a lot more than a single server can handle. Relational databases weren’t designed for this.
Companies Can’t Make Sense of It.
You need to analyze rapidly-changing, multi-structured data in real time. You don’t have the luxury of lengthy ETL processes to cleanse data for downstream reporting.
Why Other Databases Fall Short
The Internet of Things generates new streams of data that were unimaginable a decade ago, both in variety and quantity. But this new data is only worth something if your database can keep up.
Rigid Schemas. IoT is in its infancy. As sensor and communications costs come down, functionality expectations go up. New use cases and standards require flexible and dynamic development methodologies and data storage architecture.
Scale-up Isn’t an Option. Industry analysts expect that 40 billion sensors will be embedded in everyday objects by 2020. Current generations of vehicles generate 25 GB of data per hour. The next generation will generate 250 GB per hour. Traditional data management technologies weren’t designed to handle this amount of data or rate of change.
No Command. No Control. Analyzing, visualizing, and responding to sensor output (e.g., real-time supply chains, manufacturing process control) requires powerful tools that can run complex, low-latency queries across rapidly-changing data sets.
How MongoDB Enables IOT Applications
Organizations are using MongoDB for IoT because it lets them store any kind of data, analyze it in real time, and change the schema as they go.
New Devices and Data. MongoDB’s document model enables you to store and process data of any structure: events, time series data, geospatial coordinates, text and binary data, and anything else. You can adapt the structure of a document’s schema just by adding new fields, which makes it simple to handle the data generated by IoT applications.
Horizontal Scalability. MongoDB’s automatic sharding distributes data across fleets of commodity servers, with complete application transparency. With multiple options for scaling (including range-based, hash-based, and location-aware sharding) MongoDB can support thousands of nodes, petabytes of data, and hundreds of thousands of ops per second—without requiring you to build custom partitioning and caching layers.
In-Place Analytics. With its rich index and query support, including secondary, geospatial, and text search indexes, the aggregation framework and native MapReduce, MongoDB can run complex ad-hoc or reporting analytics in-place against sensor data.
Robust authentication, authorization, auditing, and encryption controls protect valuable sensor data and the analytics delivered from it.
Click here to read our previous blog post, which explores more about the fundamental concepts and assumptions that underlay the architecture of MongoDB.