BigPanda revealed the power of its Event Enrichment Engine that enriches raw alerts with rich topological and operational context to create high-quality incidents.
This key capability of BigPanda’s AIOps platform moves organizations beyond simple alert noise to turbocharging the effectiveness of event correlation, root cause analysis, and automation.
Many organizations are investing in AIOps to sift through and correlate alerts across observability and monitoring platforms to detect incidents in real-time before an incident turns into an outage. Unfortunately, many AIOps tools fail to convert raw alerts into context-rich, high-quality incidents because of their inability to easily tap and parse into all sources of contextual data that can be added to incidents as context, at scale.
“Many AIOps projects fail to live up to their promise because alerts don’t get enriched with operational, topological or other contextual data, making it difficult to separate noisy alerts from meaningful alerts, and then eliminate the noise,” said Elik Eizenberg, Co-founder and CTO at BigPanda. “Our Event Enrichment Engine is critical to improving NOC productivity and L1 resolution rates by correlating interrelated alerts into context-rich, high-quality incidents that easily describe what the problem is, what’s causing it, and what action to take.”
Cross-domain enrichment ingests and aggregates messy data across fragmented tools covering observability, monitoring, change and topology systems. These alerts are enriched with the collected topological and operational information across all technology domains, which is the linchpin for AI/ML to detect incidents in real-time as they form, surface probable root cause, and kickoff automatic workflows.
“BigPanda’s Event Enrichment Engine helps structure our event data and adds more context to alerts for our Operations team to triage issues quickly and achieve better alert compression rates,” said Samy Senthivel, Senior Digital Enterprise Monitoring Services Manager at Autodesk. “The element tagging capabilities within the Event Enrichment Engine also provide a clear correlation across events based on service topology and relationships.”
Capabilities of BigPanda’s Event Enrichment Engine include the ability to:
- Extract context buried inside incoming data streams and use it to enrich alerts
- Add topology data from external sources, including asset, inventory, orchestration, APM, configuration management and CMDB sources
- Configure, monitor and modify enrichment logic within a drag-and-drop UI
- Perform millions of enrichment actions every day
The Latest
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 ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
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 ...