InfluxData offers real-time collection and analysis of streaming machine data for self-learning and self-healing of systems
Erik Åsberg of eSmart Systems
InfluxData, the open source platform that aims to let developers build next-generation monitoring, analytics and IoT applications, reports that its real-time collection and analysis of time series data is bringing the benefits of machine learning systems, including real-time decision making and self-healing of systems.
Machine learning solutions are only as good as the real-time data collected and available to be ingested into learning algorithms. Their benefits can be inhibited due to lack of ability to collect and analyse streams of data in real-time for self-learning, self-healing and real-time decision making.
Machine learning data is time series data, and InfluxData delivers the providing Time Series Platform that ensures data is collected and analysed in real-time. With InfluxData, developers can train machine learning algorithms leveraging streaming data inputs to make decisions and take action in real-time.
As an example, InfluxData partner eSmart Systems’ intelligent analytics platform captures, analyses, visualises and converts real-time operational data into actionable insights to enable next-generation operational performance. It hosts InfluxData on its machine learning platform as its core for time series data collection and storage. As a result, eSmart Systems can trigger real-time actions such as issuing work orders for preventive maintenance or controlling signals to shut on/off devices.
“Time series data is vital when working with energy, and data from components measuring energy helps us predict what is going to happen when it comes to operations, asset management and preventive maintenance,” said Erik Åsberg, CTO of eSmart Systems. “With InfluxData, we collect data from a vast amount of devices ranging from RTUs and smart meters to EV-chargers and solar panels, with data down to sub-second resolution.”
InfluxData is a purpose-built platform designed to handle the explicit needs of a machine learning system which can’t be addressed with general purpose tools. It uniquely handles massive volumes of streaming data generated at rapid rates from IoT sensors and gauges, then compresses the data so it doesn’t consume storage capacity. eSmart Systems uses InfluxData as a vital component of its solution to interpret and analyse machine learning data in real-time, and to initiate real-time decisions and actions.
In another use case, Switch’s automation technology helps issuers recapture and increase card revenue by keeping cards Top of Wallet. Using best-in-class security protocols, Switch discovers where cards are used for payments, navigates to login pages, and adds new or updated payment cards on behalf of each user. This unique functionality is enabled by streaming anonymised crowdsourced site artifacts into InfluxDB for learning, analysis and support.
Mark Herring
“Understanding how these tasks are performing for the end user is important in ensuring they provide an effective solution,” said Gary Tomlinson, director of R&D at Switch. “InfluxDB makes it easy to collect feed site artifacts for streaming them into our machine learning system for analysis and support.”
“Real value is only achieved by providing answers while it still matters, and machine learning’s true potential can only be realised as it is applied to real-time streaming data,” said Mark Herring, InfluxData CMO.
“The industry is realising these needs are best served by time series databases to efficiently handle these workloads. Machine learning is one of many modern applications of InfluxData’s providing Time Series Platform, and the industry is turning to InfluxData to deliver on these data collection and storage requirements for real-time decision making.”
Metrics, events, and other time-based data are being generated at an exponential rate, as there is a growing requirement for analysing today’s complex environments. The InfluxData Platform provides a comprehensive set of tools and services to accumulate metrics and events data, analyse the data, and act on the data via powerful visualisations and notifications.
InfluxData’s InfluxDB Database is the overwhelming provider among Time Series Database management systems, according to DB-Engines’ latest results published earlier this month. InfluxData’s unique features enable customers to quickly build:
- Monitoring, alerting and notification applications supporting their DevOps initiatives
- IoT applications supporting millions of events per second, providing new business value around predictive maintenance and real-time alerting and control
- Real-time analytics applications that are focused on streaming data and anomaly detection
InfluxData has rapidly built its developer and customer base across industries – including manufacturing, financial services, energy, and telecommunications – by delivering the fastest growing open source platform that enables customers to derive better business insights, data-driven real-time actions, and a consolidated single view of their entire infrastructure – from applications to microservices, and from systems to sensors.
More than 420 customers, including Cisco Systems, Coupa Software, IBM, Houghton Mifflin Harcourt, Nordstrom, and Tesla, have selected InfluxData as their modern data platform for metrics and events. InfluxData is pioneering the shift to time series in a modern metrics and events platform, and is making it possible for customers to become data-driven and take on digital transformation initiatives.
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