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Lightstep Announces Root Cause Analysis in Three Clicks

Lightstep announced major updates to its observability solution to help developers optimize root cause analysis and simplify incident response.

With the introduction of log analysis and “Top Changes”, developer teams are able to zero-in on a single line of code to identify the cause of a regression in under a minute.

“Microservices and serverless architectures make it extremely difficult for developers to quickly assess the impact of a regression and isolate the root cause. Whether it’s due to our reliance on tribal knowledge, a lack of context, technology fatigue or red herrings that distract us from looking at the right data, this is a roadblock many developers are all too familiar with. By adding log search and aggregation, and building on our automated intelligence solutions, we’re uniquely positioned to allow any developer working on a deployment to side-step this issue and quickly pinpoint the root cause of a regression in under one minute,” said Katia Bazzi, Senior Software Engineer, Lightstep.

This update builds on Lightstep’s Service Health feature by introducing logs as part of Lightstep’s telemetry data set. As an essential part of the root cause analysis workflow, log search and aggregation help developers pinpoint a regression to a single line of code - allowing them to use the context of traces to paint a full picture of what’s changed.

With this update, Lightstep customers can:

- Identify the most frequently occurring logs in an error or latency regression

- Search across logs to narrow down the root-cause

- Investigate logs along the critical path to understand the root cause of a latency spike

In addition, Lightstep’s automated intelligence algorithms automatically surface which operations have experienced the greatest changes during a specific time period, whether it’s in-real-time, or during a deployment that occurred hours ago. “Top Changes” identifies which error rates, latency, throughput or other service level indicators (SLIs) experience the greatest change, enabling teams to streamline investigations and rapidly resolve incidents.

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Lightstep Announces Root Cause Analysis in Three Clicks

Lightstep announced major updates to its observability solution to help developers optimize root cause analysis and simplify incident response.

With the introduction of log analysis and “Top Changes”, developer teams are able to zero-in on a single line of code to identify the cause of a regression in under a minute.

“Microservices and serverless architectures make it extremely difficult for developers to quickly assess the impact of a regression and isolate the root cause. Whether it’s due to our reliance on tribal knowledge, a lack of context, technology fatigue or red herrings that distract us from looking at the right data, this is a roadblock many developers are all too familiar with. By adding log search and aggregation, and building on our automated intelligence solutions, we’re uniquely positioned to allow any developer working on a deployment to side-step this issue and quickly pinpoint the root cause of a regression in under one minute,” said Katia Bazzi, Senior Software Engineer, Lightstep.

This update builds on Lightstep’s Service Health feature by introducing logs as part of Lightstep’s telemetry data set. As an essential part of the root cause analysis workflow, log search and aggregation help developers pinpoint a regression to a single line of code - allowing them to use the context of traces to paint a full picture of what’s changed.

With this update, Lightstep customers can:

- Identify the most frequently occurring logs in an error or latency regression

- Search across logs to narrow down the root-cause

- Investigate logs along the critical path to understand the root cause of a latency spike

In addition, Lightstep’s automated intelligence algorithms automatically surface which operations have experienced the greatest changes during a specific time period, whether it’s in-real-time, or during a deployment that occurred hours ago. “Top Changes” identifies which error rates, latency, throughput or other service level indicators (SLIs) experience the greatest change, enabling teams to streamline investigations and rapidly resolve incidents.

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

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