Mezmo has added a more opinionated option to its platform for managing telemetry data that makes it simpler to add additional sources and ultimately reduce the volume data being collected to help reduce overall costs.
The Mezmo Flow tool makes via a single click implement recommendations that can reduce by as much as 40% the size of logs being created.
In addition, Mezmo is making it possible to create reusable pipeline components while at the same time making it possible to configure source data once for those pipelines.
The company has also enhanced the data profiling capabilities it provides to enable DevOps teams to better analyze structured and unstructured log data, and now makes it possible to collect and aggregate telemetry metrics such as log volume or errors on a per application, host or user-defined label.
Mezmo CEO Tucker Callaway said that approach in addition to giving DevOps teams more control over how telemetry data is stored at a time when observability costs continue to rise. It also makes it possible to surface insights without first having to index data in a separate observability platform. The overall goal is to streamline workflows in a way that reduces the total cost of storage, he added.
In addition, streamlining the amount of telemetry data being collected should also lead to more relevant alerts that enable DevOps teams to take timelier actions to prevent, for example, a potential outage, noted Callaway.
Mezmo originally created its platform to make it possible to route data to any observability or monitoring platform a DevOps team happens to employ. Mezmo Flow adds a tool for reducing the volume of that data at a time when the amount of telemetry data being generated only continues to increase exponentially as more applications and platforms are instrumented.
While many observability platforms have, in the past year, enhanced their ability to manage data effectively, it’s clear with the rise of platform engineering as a methodology for managing DevOps workflows at scale, there is an increased need to manage telemetry data. The challenge these teams will face is ensuring the right data is flowing to the right platform at the right time, noted Callaway.
As the number of DevOps teams embracing observability platforms continues to increase, so too will the number of organizations encountering that issue continue to expand. A Techstrong Research survey finds nearly half of respondents (48%) already work for organizations that practice observability regularly. A full 63% noted their organization will be making additional investments in observability over the next two years, with 21% describing those investments as significant.
The one clear thing is there is not enough data engineering expertise available to manage those workflows. DevOps teams will require access to data management platforms that were designed to address their specific requirements, versus relying on a platform that is more likely to have been designed for a data engineer, said Callaway.
Regardless of how organizations achieve that goal, however, the ability to manage DevOps successfully always comes down to the ability to successfully collect and analyze log data.
