Every day, telemetry data is emitted, processed, and then either stored or deleted from services. Unfortunately, understanding and then optimizing the telemetry data can be difficult. These difficulties lead to higher MTTR/D, burnout, and unwanted overage bills.

Google recently released its 2024 Cloud DORA (DevOps Research and Assessment) report, bringing together a decade’s worth of trends, insights, and best practices on what drives high performance in software delivery across industries of all sizes. This year’s findings take a closer look at how DevOps teams can achieve greater resilience and efficiency by adopting AI, improving team well-being, and building powerful internal platforms. ‍

Managing telemetry data effectively is a serious challenge for today’s engineering teams. In our webinar, Telemetry Data Management: Tales from the Trenches, experts from Mezmo and DZone shared practical strategies for building robust telemetry pipelines that both streamline operations and turn raw data into a strategic asset.

This is an updated version of an earlier blog post that now includes links to our documentation. Full-text searches are a marvel of modern computing. In less than a second, search engines can match a query against hundreds of millions of documents. In the early days of search engines, you often had to use specific search operators and terms to get accurate results.

You’ve likely noticed how some pizza places promise delivery in 30 minutes, or they’ll give you your money back. But what are they really promising? They’re setting a clear performance goal and backing it up with confidence. How do they measure their performance? They track how long each delivery takes. And why do they make this promise? Because fast service is key to keeping their business thriving.

During our webinar, Next Gen Log Management: Maximize Log Value with Telemetry Pipelines, we discussed how you can take your log management strategy to the next level with telemetry pipelines and unlock the full potential of your data. Bill explained that the rapid growth of log data is driving up storage and management costs. He emphasized the need for an intelligent, adaptable log management system to efficiently handle this situation.

One challenge for the widespread adoption of telemetry pipelines for SRE teams within an organization is knowing where to start when building a pipeline. Faced with a wide assortment of sources, processors, and destinations, setting up a telemetry pipeline can seem like trying to build a Lego set without any instructions. The solution is to provide teams with pre-defined components that provide specific functionality, that they can then use to build pipelines that meet their own requirements.

Alerts that you receive from your observability tool are based on conditions that existed seconds to minutes in the past, because the alert is only triggered after the data has been indexed within the tool. This means that your ability to take timely action in response to the condition is significantly limited, and often your window of opportunity to react is past by the time you receive the alert.

Once upon a time, we thought “Log everything” was the way to go to ensure we have all the data we needed to identify, troubleshoot, and debug issues. But we soon had new problems: cost, noisiness, and time spent sifting through all that log data. Enter log analysis tools to help refine volumes of log data and differentiate signal from the noise to reduce mental toil to process. Log beast tamed, for now….

Once upon a time, we thought “Log everything” was the way to go to ensure we have all the data we needed to identify, troubleshoot, and debug issues. But we soon had new problems: cost, noisiness, and time spent sifting through all that log data. Enter log analysis tools to help refine volumes of log data and differentiate signal from the noise to reduce mental toil to process. Log beast tamed, for now….