Operations | Monitoring | ITSM | DevOps | Cloud

Enterprises are getting increasingly tired of feeling locked into vendors, and rightfully so. As soon as you put your observability data into a SaaS vendors’ storage, it’s now their data, and it’s difficult to get it out or reuse it for other purposes. As a result, strategic independence is becoming increasingly important as organizations decide what data management tools they’re going to invest time and resources into.

Cost optimization has been one of the hottest topics in observability (and beyond!) lately. Everyone is striving to be efficient, spend money wisely, and get the most out of every dollar invested. At Logz.io, we recently embarked on a very interesting and fruitful data volume optimization journey, reducing our own internal log volume by a whopping 50%. In this article, I’ll tell you how exactly we achieved this result.

With governments doubling down on logging compliance, many public sector organizations have been focusing on optimizing their log management, especially to ensure they retain logs for required periods of time. Logs — though seemingly straightforward — are the backbone of many mission-based use cases and therefore have the potential to accelerate mission success when centrally organized and leveraged strategically. In public sector, logs are instrumental in.

Log events come in all sorts of shapes and sizes. Some are delivered as a single event per line. Others are delivered as multi-line structures. Some come in as a stream of data that will need to be parsed out. Still, others come in as an array that should be split into discrete entries. Because Cribl Stream works on events one at a time, we have to ensure we are dealing with discrete events before o11y and security teams can use the information in those events.

As the amount of telemetry being collected continues to grow exponentially, businesses are continuously seeking cost-effective ways to monitor and analyze their systems. Data collection and monitoring can be expensive, especially when dealing with large volumes of logs. One approach to maintaining visibility while reducing the amount of data collected is through creating log-based metrics.