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Data ingestion capabilities augur greater role for NoSQL in IoT

Verizon's ThingSpace IoT platform uses a Couchbase NoSQL system to help provide reliable data ingestion and flexible development. That is key as IoT devices proliferate.

All the data that pours in from the internet of things needs to go somewhere. Increasingly, that somewhere may be a NoSQL database.

While NoSQL does not provide the same tight data consistency that SQL databases do, the simplicity and resultant speed of NoSQL can lead to predictable processing performance that may elude established SQL approaches.

These schema-less or schema-lite NoSQL databases are finding a home in real-time data streaming, especially in internet of things (IoT) applications that sorely stress traditional data ingestion capabilities. Verizon's inclusion of Couchbase Server NoSQL software as part of its ThingSpace platform for IoT is a case in point.

"With the internet of things, you are making decisions and taking action in real time," said Mohanraj Umapathy, director of IoT platform work at Verizon Labs. "A key part of that is a database that can support those capabilities."

Data ingestion and SLAs

Umapathy said Couchbase Inc.'s NoSQL approach enables ThingSpace to better meet service-level agreements (SLAs). The Couchbase software can support key-value architecture that handles data ingestion at a predictable rate, so that overall performance can be reliably endured. That takes on additional importance as more and more devices are connected via IoT.

What we've personally found useful in Couchbase is that it acts as a document database.
Mohanraj Umapathydirector of IoT platform work, Verizon Labs

Unlike SQL, which can have strict upfront requirements that call for explicit data schema, this NoSQL approach provides more ways to manage data. Such flexible adaptation is especially useful due to the rapid changes common with connected devices and application development today.

"What we've personally found useful in Couchbase is that it acts as a document database, and we don't have to specify its schema upfront. That allows us to add fields as we go," Umapathy said.

Incoming data is summarized into a JSON format, according to Umapathy, and updates can be performed on the fly.

He positioned the Couchbase NoSQL software as part of an overall real-time analytics system that includes Kafka connectors for message handling and Spark. Like other applications working from a similar pallet of big data ecosystem components, Verizon's ThingSpace combines NoSQL data ingestion with Spark data processing for high-speed analytics.

Other Couchbase benefits are support for cross-data center deployment and support for REST APIs. The latter trait allows users to more easily set up event processing and predictive analytics jobs, Umapathy said.

Endpoints on the march

IoT has been a major push for communications giant Verizon, which has fashioned its ThingSpace platform as an API-enabled, one-stop shop for telematics and analytics, as well as device and data management related to IoT. Last month, when Verizon rolled out a 2.4 million-square-mile-sized 4G communications network with wireless access for IoT, it included a chipset that implements ThingSpace clients.

The company sees IoT as a fertile extension of the cellphone business, pointing to IDC estimates that the installed base of IoT endpoints will grow to more than 25.6 billion in 2019. Such device growth in the field puts a premium on scalable processing on the back end.

"The ThingSpace platform is meant to allow customers to build their IoT applications quickly, but in a scalable way," said Umapathy, who noted intelligent pallet and package tracking, agriculture and telematics among early candidate application types that will spawn more connected devices.

These and other use cases could cause a greater crush of incoming data, and, in turn, greater use of NoSQL databases than has been seen to date for data ingestion and other purposes.

Next Steps

Learn about the upsides and downsides to data and IoT

Discover the link between predictive maintenance and IoT

Find out about data ingestion and Hadoop

This was last published in April 2017

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