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Elastic Appoints New GM for Observability

Elastic announced the appointment of Abhishek Singh as general manager for Observability.

Singh will oversee the evolution of Elastic Observability, which converges all types of telemetry data, including metrics, logs, and traces for unified visibility and actionable AI-powered insights. He will help spearhead the delivery of rapid advancements like the Elastic AI Assistant for Observability, which leverages the power of generative AI and an organization’s own proprietary data to make problem resolution faster and easier for site reliability engineers (SRE) and DevOps engineers.

“Abhishek is a proven leader who will bring considerable experience and knowledge to Elastic as we innovate on the most widely deployed observability solution, built on the ELK Stack,” said Ken Exner, chief product officer. “His expertise across both product and engineering provides him with broad industry insight that will help us grow strategically and technically as a business.”

Prior to joining Elastic, Singh was vice president of Product at Datadog for approximately one year and brings with him 18 years of strategic and technical expertise gained in product design and development leadership roles, including at AWS, BlackRock, and Webscan. During his seven years at AWS, Singh was general manager for its observability solution, AWS X-Ray, in a role that spanned both business and engineering responsibilities.

“I’ve been impressed by Elastic’s innovative use of generative AI which allows an organization to use their proprietary data for context awareness that leads to more accurate solutions,” said Singh. “I look forward to working with Elastic to help enterprises break down silos by bringing together infrastructure, application, user, and business telemetry for end-to-end observability on a single AI-powered platform.”

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Elastic Appoints New GM for Observability

Elastic announced the appointment of Abhishek Singh as general manager for Observability.

Singh will oversee the evolution of Elastic Observability, which converges all types of telemetry data, including metrics, logs, and traces for unified visibility and actionable AI-powered insights. He will help spearhead the delivery of rapid advancements like the Elastic AI Assistant for Observability, which leverages the power of generative AI and an organization’s own proprietary data to make problem resolution faster and easier for site reliability engineers (SRE) and DevOps engineers.

“Abhishek is a proven leader who will bring considerable experience and knowledge to Elastic as we innovate on the most widely deployed observability solution, built on the ELK Stack,” said Ken Exner, chief product officer. “His expertise across both product and engineering provides him with broad industry insight that will help us grow strategically and technically as a business.”

Prior to joining Elastic, Singh was vice president of Product at Datadog for approximately one year and brings with him 18 years of strategic and technical expertise gained in product design and development leadership roles, including at AWS, BlackRock, and Webscan. During his seven years at AWS, Singh was general manager for its observability solution, AWS X-Ray, in a role that spanned both business and engineering responsibilities.

“I’ve been impressed by Elastic’s innovative use of generative AI which allows an organization to use their proprietary data for context awareness that leads to more accurate solutions,” said Singh. “I look forward to working with Elastic to help enterprises break down silos by bringing together infrastructure, application, user, and business telemetry for end-to-end observability on a single AI-powered platform.”

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

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Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...