40 billion sensors. $19 trillion in revenue.
You’re gonna need a bigger database.
Sensor-enabled objects are unleashing torrents of data previously unimaginable. Production lines to patrol cars to pacemakers. New revenue beckons. But sensor data is only useful if you can do something with it.
With MongoDB, you can make sense of sensor data, building applications never before possible. Faster. With less money.
Bosch has built its Internet of Things suite on MongoDB, bringing the power of big data to a new range of Industrial Internet applications including manufacturing, automotive, retail, energy and many others. Learn More.
|IoT Is Hard||MongoDB Makes it Easy|
|Can’t Stay Ahead. Each new generation of ‘thing’ 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.||Do the Impossible. MongoDB lets you build apps you could never build before. It can manage data of any structure, no matter how often it changes. You can ship new functionality without redesigning your database.|
|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.||Scale Big. MongoDB is built to scale out on commodity hardware, in your data center or in the cloud. Serve millions of users and billions of sensors without complex hardware or extra software. As sensor data grows, so does MongoDB.|
|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.||Signal vs. Noise. MongoDB can analyze data of any structure. It can do so directly within the database, giving you results in real time, and without expensive data warehouse loads.|
Using sensor data to manage the production line
Image courtesy of Bosch, a MongoDB customer
Why Other Databases Fall Short
The Internet of Things generates new streams of data previously unimaginable, 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 Makes it Easy
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, making it simple to handle the rapidly changing 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.
- Security. Robust authentication, authorization, auditing and encryption controls protect valuable sensor data and the analytics delivered from it.