IoT time series data is ‘of the hour’, says InfluxData
IoT time series data is 'of the hour', InfluxData

IoT time series data is ‘of the hour’, says InfluxData

InfluxData, which specialises in time series data management, has shared details of recent customer implementations with a particular focus on IoT. 

Time series data is a big deal in the IoT. A time series is a series of data points collected at regular intervals and indexed in time order – the sort of reading you might see, for example, from a smart electricity meter in a home or from meteorology kit for forecasting the weather.

It is data that typically arrives in volume and requires careful handling. InfluxData may be one of many data analytics players, but with its focus on time series data, it’s a significant player in a small, but important subsector of that market. Its open source software enables developers to build monitoring, analytics and IoT applications.

The company’s product proposition centers on deployments where the data output of IoT devices and sensors sometimes necessitate changes to systems in near real time; that is, where developers will need to make changes or updates to IoT systems in order to address how those devices and sensor behave.

Read more: Basho CTO: It’s high IoT time for time series data

Unique? Well, different

The company says that IoT data is ‘unique’, in that sensors deliver time-stamped or time series data in order to measure change over time – for example, a rise in energy consumption when a family returns home or the different speeds of gusting winds over the course of a particularly stormy night.

So InfluxData works to provide managers of IoT deployments with a direct view into the state of each device at any given point in time.

Bboxx, for example, is a venture-backed company developing software to provide affordable, clean energy to off-grid communities in the developing world. Its core product is a plug-and-play solar system and the company is an InfluxData customer.

“Bboxx collects performance data from its portfolio of 50,000-plus IoT solar home systems currently deployed across East Africa,” explains David McLean, the company’s lead developer. “Performing real-time analytics on each incoming data stream, [we] can monitor system performance, analyze customer usage patterns and alert on unit failures or customer tampering events.”

Spiio, another InfluxData customer, provides a sensor and software solution for remote monitoring of vertical living ‘green walls’ and other high-value plant installations. Spiio uses sensors to understand plant performance from data.

“InfluxDB was a tech enabler for our vision of bridging the gap between things and people,” says Jens-Ole Graulund, chief technology officer at Spiio. “Having permanent access to time series data and plant analytics, InfluxData reveals trends and enables data-driven decisions, not only for green wall maintenance but also for its design – for green walls built to perform.”

Read more: Honeywell launches analytics hub to ‘listen’ to smart buildings

It’s time for time series data

So is time series data ‘of the hour’ when it comes to the IoT? Well yes, but InfluxData is by no means unique its willingness to embrace this subset of data analytics.

Hitachi Vantara (specifically within its Pentaho product line), works in this space, as do other companies, including Redis Labs, Microsoft (part of the Azure cloud is specifically offered in an optimization format for time series jobs) and IBM. Then there’s a whole host of small time series database specialists, too: RRDTool, Graphite, Prometheus and Druid. 

Regardless of who is spinning the spin about the importance of time series data, what they tend to have in common is an emphasis on building custom logic, user-defined functions (UDFs) and machine learning libraries. These allow customers to perform streaming analytics on time series IoT data and create alerts when dynamic thresholds are reached.

But InfluxData is clearly making a few waves, with a customer roll call that includes automaker Tesla, US department chain Nordstrom, and online auction site Ebay. Maybe it’s time to keep a closer eye on time series data. 

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