Customer Use Cases

Get insights into how various organizations are using InfluxData to solve the problem of monitoring metrics and events in real-time across any use-case.

IoT & Sensor Monitoring

Instrumentation of every available surface in the material world, from machines to humans is a reality. Everything inside and outside the compute is emitting a relentless stream of metrics and events. The sheer volume of data is astronomical, and growing exponentially, creating unique opportunities for businesses that can monitor and react to this data in real-time. Traditional monitoring solutions just don’t have the speed, footprint, and uptime needed for this market.

DevOps Monitoring

Emerging trends like microservices, containerization, elastic storage, Software Defined Networking, Hybrid clouds, etc, all keep pushing the boundaries of what constitutes DevOps Monitoring. The number of endpoints and the variety of metrics that need monitoring forces a reconsideration of the one-size fits all mentality. Modern DevOps monitoring needs to be flexible enough to handle unique application and infrastructure metrics on a common framework.

Real-Time Analytics

The volume of metrics and events is beyond what any human is able to realistically interpret and take action. Machine learning with real-time analytics is crucial in finding the “signal from the noise”. Whether the organization needs real-time analytics to buy and sell equities, perform predictive maintenance on a machine before it fails, adjust prices based on customer behavior – processing, analyzing and acting on the time-series data in real-time is the problem to solve.


The Grid Systems Infrastructure team at SolarCity is responsible for providing the infrastructure to withstand loading tons of real-time data from their field devices, into their messaging system, and back out to multiple outlets, such as real-time databases like InfluxDB. SolarCity loves continuous queries (CQs), because it allows them to aggregate from InfluxDB in real-time.

High Availability, Easy scaling and Fast response times are the main features that BBOXX needs. They are able to store data from 85,000+ of units yet query any individual one with low latency.

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The NIST ARTIQ project uses InfluxDB as a backend to log and analyze everything about their quantum physics experiments: from vacuum pressures, environmental parameters, and laser powers, to qubit transition frequencies, single ion fluorescence, and quantum gate fidelities.


eBay’s Experimentation environment enables users to answer important analytics and business questions. This testing allows business units to conceptually explore new ideas that can vary from subtle to radical variations. Such test variations can be easily targeted to segments of the total customer population providing a level of assurance before launching to a broader audience. InfluxDB along with Grafana is used to measure various data quality metrics within the Experimentation platform that are monitored daily.

Nordstrom uses InfluxDB to support their company-wide operational metrics needs. What did Nordstrom find compelling about InfluxDB? It was fast, used less space and has rich retention policy support. By leveraging the entire TICK-stack (Telegraf, InfluxDB, Chronograf and Kapacitor), it gave Nordstrom a full life cycle of data management capabilities for their time-series data.

InfluxDB provides persistent time-series data storage for the monitoring of Cisco’s renewals eCommerce application.

Real-Time Analytics

InfluxDB is at the heart of GfK’s a new research platform that processes metrics in real time. More specifically, InfluxDB and telegraf are used to feed real time metrics from kafka for visualization into Grafana.

DNSFilter needed a solution that could grow and scale as their business grew. Because they have a global anycast network that needs to support the collection of billions of queries per day, InfluxDB provided them with the collection and querying capabilities to power their dashboard analytics and metered-based billing. InfluxDB helps to power the very features that differentiates DNSFilter’s service from the competition.

InfluxDB is insanely fast, friendly and capable. And like most of our backend services, it’s written in Go. Any developer can jump in and start querying and creating data with a minimal amount of background knowledge. It’s easy to instrument and starts tracking new data points with just a few minutes of work, and we’ve grown confident that InfluxDB can take whatever we throw at it. In short, it scales across servers, developers, and business requirements. Integration into visualization tools like Grafana is a big plus too.

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