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LightStep Releases Service Health for Deployments

LightStep announced the release of its Service Health for Deployments solution to help developers quickly and easily identify and remediate service health issues during and after a deployment.

With LightStep’s solution, understanding service health has never been easier. Developers can monitor performance regressions to their services during and after a deployment, gaining visibility into the latency, error ratio, and throughput changes to their operations. Additionally, developers are able to understand why a regression has occurred, with rich aggregate trace analysis functionality such as latency histogram comparisons, operations diagrams, and an automated correlation engine for performing rapid root cause analysis. Users can also ensure they are getting the most value from their services by demystifying and iterating on proper instrumentation of their service with LightStep’s Instrumentation Quality Score.

"As developers ourselves, we know that deploys often result in regressions, and investigating the cause can be a time consuming process," said Kay Ousterhout, Software Engineer, LightStep. "Our solution takes the uncertainty and guesswork out of service deployments so our customers can focus on shipping quality applications faster."

Following a regression, Service Health for Deployments enables users to quickly perform rich root cause analysis to identify what went wrong. In addition to reactive investigation, users are able to proactively monitor deployments. Using LightStep, they’re able to:

- Compare performance before, during, and after a deployment

- Compare latency distributions to estimate the size and scope of a regression

- Correlate tags that have the biggest impact on latency

- Provide visibility into the complete operation and service diagrams with critical path latency mapped to each operation or service

- Perform aggregate trace analysis to identify what’s driving a regression

Microservices have become ubiquitous among enterprise development teams. According to research LightStep conducted, about 9 in 10 enterprise development teams are currently using or plan to use microservices. Unfortunately, when microservices scale, systems grow exponentially complex - making it extremely difficult for developers to understand why services fail. LightStep Service Health for Deployments empowers developers with the ability to seamlessly navigate evolving end-to-end application stacks so they can quickly identify and resolve service health issues before they impact the customer experience.

LightStep Service Health for Deployments is generally available.

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LightStep Releases Service Health for Deployments

LightStep announced the release of its Service Health for Deployments solution to help developers quickly and easily identify and remediate service health issues during and after a deployment.

With LightStep’s solution, understanding service health has never been easier. Developers can monitor performance regressions to their services during and after a deployment, gaining visibility into the latency, error ratio, and throughput changes to their operations. Additionally, developers are able to understand why a regression has occurred, with rich aggregate trace analysis functionality such as latency histogram comparisons, operations diagrams, and an automated correlation engine for performing rapid root cause analysis. Users can also ensure they are getting the most value from their services by demystifying and iterating on proper instrumentation of their service with LightStep’s Instrumentation Quality Score.

"As developers ourselves, we know that deploys often result in regressions, and investigating the cause can be a time consuming process," said Kay Ousterhout, Software Engineer, LightStep. "Our solution takes the uncertainty and guesswork out of service deployments so our customers can focus on shipping quality applications faster."

Following a regression, Service Health for Deployments enables users to quickly perform rich root cause analysis to identify what went wrong. In addition to reactive investigation, users are able to proactively monitor deployments. Using LightStep, they’re able to:

- Compare performance before, during, and after a deployment

- Compare latency distributions to estimate the size and scope of a regression

- Correlate tags that have the biggest impact on latency

- Provide visibility into the complete operation and service diagrams with critical path latency mapped to each operation or service

- Perform aggregate trace analysis to identify what’s driving a regression

Microservices have become ubiquitous among enterprise development teams. According to research LightStep conducted, about 9 in 10 enterprise development teams are currently using or plan to use microservices. Unfortunately, when microservices scale, systems grow exponentially complex - making it extremely difficult for developers to understand why services fail. LightStep Service Health for Deployments empowers developers with the ability to seamlessly navigate evolving end-to-end application stacks so they can quickly identify and resolve service health issues before they impact the customer experience.

LightStep Service Health for Deployments is generally available.

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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 ...

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 ...