Large IoT environments are highly complex and comprise multiple layers of disparate devices that must move data between each other, across potentially unreliable connections. Having visibility into each layer of your IoT environment is critical for quickly identifying problems with your deployment that could negatively impact user experience.

The structured nature of Kubernetes enables a repeatable and scalable means of deploying and managing services and applications. This has led to widespread adoption across market verticals for both on-premises and cloud deployment models. The autonomous nature of Kubernetes operation, however, demands comprehensive, fully-converged observability and security. This is uniquely possible today using the Elastic platform.

The observability market is maturing. This evolution is clearly visible in the rise of OpenTelemetry, an open source framework for application performance monitoring and observability.

Tracing has always been a key use case for time series data. But admittedly, it’s also one that past versions of InfluxDB could not handle as well as we wanted. One of the roadblocks was the cardinality issue. Tracing data is, almost by definition, high cardinality data and prior to InfluxDB IOx, high cardinality data could affect query performance.