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Elastic Expands Kibana Training

Elastic, the company behind Elasticsearch and the Elastic Stack, released an expanded version of its Kibana training course in response to increased demand and new features.

The Elastic instructor-led course prepares technical or non-technical professionals, data analysts, security analysts, operations analysts, DevOps and others to harness Kibana to visualize, manage and analyze data in Elasticsearch.

Fresh content in the revised course, which has doubled in length to 16 hours, covers new Kibana topics like Canvas, query bar, spaces, advanced settings and much more.

Starting with the fundamentals, Elastic instructors will guide participants through the core concepts of data analysis using Kibana — from simple aggregation-based charts to complex time-series visualizations. No prior knowledge of Kibana or the Elastic Stack is required.

By the end of these lectures, labs and Q&A sessions, Kibana course participants come away with the knowledge to easily find answers and anomalies with data in Elasticsearch.

Participants learn how to create visualizations and dashboards across a variety of data sets. Students keep course materials to continue learning on their own, and they become empowered to manage Kibana by creating users, roles and spaces.

The Kibana Data Analyst course, redesigned from the ground up, now includes twice the hours of training and builds on Elastic's former 8-hour offering.

Elastic's Kibana course is available now and includes 16 hours of live, instructor-led training in a virtual setting. Corporate teams, now for the first time, can also request a private Kibana training course taught by an Elastic instructor at the convenience of their worksite — eliminating the time and expense associated with travel.

Elastic is constantly refreshing and adding new courses to our portfolio of training offerings. These include a series on becoming an "Elastic Certified Engineer," in addition to highly focused and specialized training courses on "I didn't know it could do that," and so much more.

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 ...

Elastic Expands Kibana Training

Elastic, the company behind Elasticsearch and the Elastic Stack, released an expanded version of its Kibana training course in response to increased demand and new features.

The Elastic instructor-led course prepares technical or non-technical professionals, data analysts, security analysts, operations analysts, DevOps and others to harness Kibana to visualize, manage and analyze data in Elasticsearch.

Fresh content in the revised course, which has doubled in length to 16 hours, covers new Kibana topics like Canvas, query bar, spaces, advanced settings and much more.

Starting with the fundamentals, Elastic instructors will guide participants through the core concepts of data analysis using Kibana — from simple aggregation-based charts to complex time-series visualizations. No prior knowledge of Kibana or the Elastic Stack is required.

By the end of these lectures, labs and Q&A sessions, Kibana course participants come away with the knowledge to easily find answers and anomalies with data in Elasticsearch.

Participants learn how to create visualizations and dashboards across a variety of data sets. Students keep course materials to continue learning on their own, and they become empowered to manage Kibana by creating users, roles and spaces.

The Kibana Data Analyst course, redesigned from the ground up, now includes twice the hours of training and builds on Elastic's former 8-hour offering.

Elastic's Kibana course is available now and includes 16 hours of live, instructor-led training in a virtual setting. Corporate teams, now for the first time, can also request a private Kibana training course taught by an Elastic instructor at the convenience of their worksite — eliminating the time and expense associated with travel.

Elastic is constantly refreshing and adding new courses to our portfolio of training offerings. These include a series on becoming an "Elastic Certified Engineer," in addition to highly focused and specialized training courses on "I didn't know it could do that," and so much more.

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 ...