Skip to main content

Lightstep Releases Change Intelligence

Lightstep announced the general availability of Change Intelligence.

While AIOps is frequently advertised as a way to dramatically improve IT operations, it often fails to deliver, in part because today’s software applications are too dynamic: software, infrastructure, and user behavior are all changing faster than ever before. Lightstep has embraced those changes and put them at the center of analyzing application performance with Change Intelligence, inspired by work done by the Lightstep CEO Ben Sigelman on Google’s Monarch project.

The X-factor that makes Change Intelligence possible is Lightstep’s time-series database, which can process over a trillion events each day and is built by the same engineers that worked on the Monarch project at Google. Monarch is the globally-distributed in-memory time series database system in Google, that is used internally to monitor the availability, correctness, performance, load, and other aspects of billion-users scale applications and systems at Google. By tightly integrating these metrics with Lightstep’s existing distributed tracing data, engineers can connect cause and effect faster than what was previously possible.

“We took inspiration from the technology we built at Google, took it to the next level, and made it generally available to all Lightstep users,” said Ben Sigelman, Co-Founder and CEO of Lightstep. “With Change Intelligence, any developer, operator, or SRE can instantly understand changes in their service’s health and – most importantly – what caused those changes. In this way, we’re able to actually deliver on the promise of AIOps: to automate the process of investigating changes within complex systems.”

“The truth is that companies are already drowning in data from dashboards, alerts, endless logs – as an industry we’re so afraid of missing data that we collect all of it,” said Daniel ‘Spoons’ Spoonhower, Co-Founder and Chief Architect of Lightstep. “But from talking with customers, we’ve learned that this can create just as many problems as it solves. We’re not looking to add more data or noise – we’re looking to find root causes and resolve issues faster.”

The Latest

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 ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

Lightstep Releases Change Intelligence

Lightstep announced the general availability of Change Intelligence.

While AIOps is frequently advertised as a way to dramatically improve IT operations, it often fails to deliver, in part because today’s software applications are too dynamic: software, infrastructure, and user behavior are all changing faster than ever before. Lightstep has embraced those changes and put them at the center of analyzing application performance with Change Intelligence, inspired by work done by the Lightstep CEO Ben Sigelman on Google’s Monarch project.

The X-factor that makes Change Intelligence possible is Lightstep’s time-series database, which can process over a trillion events each day and is built by the same engineers that worked on the Monarch project at Google. Monarch is the globally-distributed in-memory time series database system in Google, that is used internally to monitor the availability, correctness, performance, load, and other aspects of billion-users scale applications and systems at Google. By tightly integrating these metrics with Lightstep’s existing distributed tracing data, engineers can connect cause and effect faster than what was previously possible.

“We took inspiration from the technology we built at Google, took it to the next level, and made it generally available to all Lightstep users,” said Ben Sigelman, Co-Founder and CEO of Lightstep. “With Change Intelligence, any developer, operator, or SRE can instantly understand changes in their service’s health and – most importantly – what caused those changes. In this way, we’re able to actually deliver on the promise of AIOps: to automate the process of investigating changes within complex systems.”

“The truth is that companies are already drowning in data from dashboards, alerts, endless logs – as an industry we’re so afraid of missing data that we collect all of it,” said Daniel ‘Spoons’ Spoonhower, Co-Founder and Chief Architect of Lightstep. “But from talking with customers, we’ve learned that this can create just as many problems as it solves. We’re not looking to add more data or noise – we’re looking to find root causes and resolve issues faster.”

The Latest

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 ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...