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Sumo Logic Supports Docker Enterprise

Sumo Logic announced support for Docker Enterprise to provide joint customers with improved application performance monitoring across the complete application lifecycle.

Building on existing Docker capabilities, the new integration provides joint Docker and Sumo Logic customers with improved monitoring and troubleshooting of the Docker infrastructure, as well as better correlation of issues between the Docker platform and the application for faster root cause analysis. This ensures not only the health of a customer’s applications running on the Docker platform, but also the health of the Docker platform itself for complete visibility into the modern application stack.

“Docker is built on the foundations of agility, collaboration and the desire to push the boundaries of technological innovation,” said Kal De, CTO and EVP of product development, Docker. “Sumo Logic shares these same values and so our decision to use Sumo Logic internally was a natural fit. Our customers rely on us to give them the freedom to build, manage and secure applications without the fear of technology or infrastructure lock in, and Sumo Logic provides us with the continuous intelligence and data insights we need to ensure our container platform lives up to that promise.”

Furthermore, with the Sumo Logic platform, Docker quality engineering and customer support teams are given additional tools to improve the product release process and to reduce the time needed to solve customer issues. Advanced analytics, real-time alerting and customizable dashboards from Sumo Logic make it easy for teams to quickly identify issues and push code to production faster while increasing code quality for customers.

“In today’s digital economy, the pressure is on organizations now more than ever to invest in technologies that improve speed to market and deliver the best customer experiences,” said Christian Beedgen, co-founder and CTO, Sumo Logic. “Containers provide more benefits than traditional virtualization by enabling organizations to build more efficient applications that leverage microservices architecture and can be orchestrated across private and multi-cloud environments. Our goal is to empower the people who power modern business, and to have Docker employ Sumo Logic as their machine data analytics platform reinforces the value of machine data for today’s digital business and demonstrates our joint commitment to supporting the growing container platform ecosystem.”

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Sumo Logic Supports Docker Enterprise

Sumo Logic announced support for Docker Enterprise to provide joint customers with improved application performance monitoring across the complete application lifecycle.

Building on existing Docker capabilities, the new integration provides joint Docker and Sumo Logic customers with improved monitoring and troubleshooting of the Docker infrastructure, as well as better correlation of issues between the Docker platform and the application for faster root cause analysis. This ensures not only the health of a customer’s applications running on the Docker platform, but also the health of the Docker platform itself for complete visibility into the modern application stack.

“Docker is built on the foundations of agility, collaboration and the desire to push the boundaries of technological innovation,” said Kal De, CTO and EVP of product development, Docker. “Sumo Logic shares these same values and so our decision to use Sumo Logic internally was a natural fit. Our customers rely on us to give them the freedom to build, manage and secure applications without the fear of technology or infrastructure lock in, and Sumo Logic provides us with the continuous intelligence and data insights we need to ensure our container platform lives up to that promise.”

Furthermore, with the Sumo Logic platform, Docker quality engineering and customer support teams are given additional tools to improve the product release process and to reduce the time needed to solve customer issues. Advanced analytics, real-time alerting and customizable dashboards from Sumo Logic make it easy for teams to quickly identify issues and push code to production faster while increasing code quality for customers.

“In today’s digital economy, the pressure is on organizations now more than ever to invest in technologies that improve speed to market and deliver the best customer experiences,” said Christian Beedgen, co-founder and CTO, Sumo Logic. “Containers provide more benefits than traditional virtualization by enabling organizations to build more efficient applications that leverage microservices architecture and can be orchestrated across private and multi-cloud environments. Our goal is to empower the people who power modern business, and to have Docker employ Sumo Logic as their machine data analytics platform reinforces the value of machine data for today’s digital business and demonstrates our joint commitment to supporting the growing container platform ecosystem.”

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.