Flowfinity has announced the release of ‘Flowfinity Streams’, a time series database designed to store and manage large amounts of machine-generated IoT data. Flowfinity Streams is ‘fully’ compatible with their software, Flowfinity Actions, which enables workflow automation and data visualisation.
The expansion of industrial IoT applications in the enterprise presents several challenges for organisations. Firstly, it can be difficult and costly to programme IoT hardware and sensors to align with core ERP (enterprise resource planning) and SCADA (supervisory control and data acquisition) systems. Flowfinity has addressed this issue with the introduction of the M1 Controller, which offers compatibility with all Flowfinity no-code software.
Secondly, once the hardware and software have been configured for an IoT asset monitoring solution, the question arises of how to collect and store the data in a way that allows for analysis and actionability. This is where Flowfinity Streams comes in. It is designed to ingest and store large amounts of time series data from IoT sensors and other automated data sources, while using less space compared to traditional relational data storage models.
With the ability to store billions of data records, Flowfinity Streams features a highly optimised ingestion engine that can process a CSV (comma-separated values) file containing over 100 million records in just minutes, reducing processing time and resource usage. It can function as a stand-alone solution, but its true potential is unlocked through integration with Flowfinity Actions.
This integration enables the merging of machine and human-driven workflows, with Streams triggering processes from incoming data and launching workflows in Flowfinity Actions via software automation robots when specific thresholds or business rules are met.
For example, if monitoring sensor data from industrial equipment in a manufacturing or utilities setting to optimise runtime and maintenance schedules, Streams is used to accumulate usage statistics. When a threshold is reached, Streams will pass that variable to Actions where a software robot will create a preventative maintenance work order and notify the appropriate team members.
Once the maintenance has been completed Actions will automatically reset the variable in the Stream time series, setting the stage for the next maintenance period and ensuring maximum return from key assets.
Streams data can also be visualised in interactive operational dashboards to help make informed decisions, this includes step charts which allow to see data that changes but remains static between changes for conditional monitoring of equipment status, as well as in maps. It will specify when and for how long a machine went down or surpassed its ideal thresholds.
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