Imply introduced Imply Lumi, an Observability Warehouse — a high-performance, cost-efficient data layer built to plug into existing observability tools with zero disruption.
“Decoupling the observability stack gives teams the freedom to do more while spending less,” said Fangjin Yang, CEO and co-founder of Imply. “Just as decoupling transformed business intelligence, Imply Lumi brings that same flexibility and control to observability — without requiring teams to abandon the tools they already rely on.”
At launch, Imply Lumi includes native integrations with Splunk, Grafana Labs, Tableau, and AI assistants like Claude and Langchain — extending observability data into both dashboards and AI-powered interfaces.
"We’ve proven that Imply Lumi can take log data, optimize it for faster searches, and store it more efficiently than traditional formats — and that has our partners and early adopters excited,” said Eric Tschetter, Chief Architect at Imply. “With these innovations, teams can keep all their logs in a format that works seamlessly with Splunk, combining efficiency, speed, and compatibility in one solution.”
As a recognized member of the Splunk Partnerverse, Imply is working alongside Splunk’s ecosystem of technology and services partners to extend the value of Splunk deployments. By deploying Imply Lumi alongside Splunk, customers can keep using the tools they know while gaining the scale and efficiency they need to meet today’s observability demands.
Imply Lumi works with what you already have — including Splunk Universal Forwarders, Heavy Forwarders, and OpenTelemetry — via S2S or S3. No custom agents. No pipeline rewrites.
Imply Lumi integrates as a Splunk-compatible federated provider, letting teams run native SPL (Search Processing Language) queries directly from the Splunk UI or API. Dashboards, alerts, and workflows stay exactly the same — just backed by Imply Lumi’s optimized data store.
The Latest
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 ...
When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...
Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...
Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...
As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...
For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...