Internet of Things (IoT)

Unlock the business potential of connected devices

IoT database platform
Elastic DB Service for Sensor Data

Description

An application that keeps track of sensors deployed at various geo-locations as well as the event data emitted by them. The data from the sensors needs to be processed, stored and quickly analyzed.

Requirements

The typical IoT application architecture consists of a variable set of devices/sensors that collect data. This data is transported and stored in a backend datacenter using a distributed messaging system. The data flows from the messaging store in the backend datacenter into a stateless processing tier, which transforms the data according to the business logic. The transformed data is written to a database for serving to the end users or for consumption by other services using APIs.

  • The number of deployed devices and the ingest rate can vary rapidly, requiring the application and the database to scale out (or shrink) reliably and efficiently.

  • The application has a high write rate and ever growing data sets due to the always-on nature of these connected devices.

  • Data that is collected is often analyzed and the result of the analysis is presented to end users or other systems using APIs. The database should be integrated with good frameworks for data analysis like Apache Spark.

  • Data comes from different geographic locations, and may need to be processed in different datacenters.

IoT database platform

YugaByte DB
Why YugaByte DB?

Reliable, Fast Scale-Out

Reliable, Fast Scale-Out

Scale out operational DB tier reliably and efficiently without any impact to the currently running application. Dynamically resize the cluster and change machine types without any interruption to the application.

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Automatic Tiering of Colder Data

Automatic Tiering of Colder Data

Efficient handling of large, ever-growing datasets. Automatic tiering of colder data to cheaper storage as well as easy expiration of older data.

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Strong Consistency with High Performance

Integrated with Apache Spark and the SMACK stack

Natively integrated with Apache Spark for fast analytics. Stores data in a time-partitioned manner making it very efficient at retrieving recent data.

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Flexible Geo-Partitioned, Multi-Datacenter Deployments

Flexible Geo-Distributed, Multi-Datacenter Deployments

Replicate data using sync or async replication across multiple datacenters for both write and read scalability. Deliver high write availability even in a cross-datacenters configuration (as long as majority of replicas are available). Satisfy compliance requirements easily through one-click deployment of async replicas in far away regions.

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Operational Simplicity For Fast Data Apps Like Never Before