Operations | Monitoring | ITSM | DevOps | Cloud

You already use Cribl to tame your telemetry data. Now you can turn that data into apps your teams actually want to use. In this video, we walk through how to create apps in the Cribl Platform and show how real apps solve real problems: guided troubleshooting for noisy incidents, opinionated security views, and exec-friendly ROI dashboards. You’ll see how apps sit on top of Cribl Stream, Edge, Search, and Lake, so you reuse the data and logic you already have instead of building custom tools from scratch.

See how Bring Your Own Storage (BYOS) in Cribl Lake allows teams to connect directly to Azure Blob Storage and instantly search data in place — without moving, duplicating, or rehydrating telemetry. In this demo, Cribl Product Manager Risk Salsa walks through setup, dataset creation, and how to run fast investigations across your Azure-hosted data using Cribl Search.

This is an excerpt from a real AI team weekly meeting where we talk about the stuff we build and occasionally also demo them! In this one, Principal Software Engineer Sven Großmann shows a new feature he's working on for AI Observability, called "guards". We're showing parts of our team meetings to build in public in some small way and give you a sneak preview of what's to come. But not all features we show may make it to production! You've been warned. :)

The wait is over for Azure-first organizations. Cribl just launched Cribl Lake Bring Your Own Storage (BYOS) for Microsoft Azure, giving you full data lake power without moving a single byte of telemetry out of your environment. Join us to see how you can finally get the flexibility of a modern data lake while keeping your data in Azure.

In this episode, Leon explores a bunch of BYO (bring your own) enhancements to Cribl including the ability to bring your own AI model, storage, and more.

Your SIEM only holds a slice of your telemetry. Your data lake holds the rest. We'll show you how to use that to your advantage for investigations, threat hunting, and reporting. Why your data lake beats your SIEM for investigations – Your SIEM keeps a short window of expensive, filtered data. Your data lake keeps everything. When something goes wrong, that difference matters more than you think Threat hunting without the handcuffs – Hunting across months of data in a SIEM is painful and costly. We'll show you how a well-planned lake makes broad, deep searches practical and affordable.

Investigations are time-sensitive, and analysts shouldn’t waste time recreating the same workflows or rewriting familiar queries. Whether troubleshooting infrastructure, investigating suspicious IPs, or analyzing host activity, teams often rely on duplicating old processes and copying query snippets — a slow, inconsistent approach that’s hard to scale.

Honeycomb and Embrace are extending the rigorous, data-driven practice that Honeycomb pioneered for foundational to mobile and web, giving, site reliability, and platform teams a complete, correlated picture of system health. The strategic partnership makes understanding performance and reliability for every user and every screen part of the observability practice, bringing new depth and standardization to how teams measure end user impact.

Watch a full replay of all sessions on Day 3 of Honeycomb's Innovation Week.

Honeycomb has shipped a production integration with Amazon Bedrock AgentCore, surfacing agent telemetry directly in Agent Timeline, Honeycomb's trace view for behavior. It's available now and built on.

If you're building agents trying to figure out the best way to actually make them successful in production, you're going to want to know about headless observability. Headless observability means an agent can access information about the health of your system through a CLI instead of clicking around dashboards. It's the data layer that going to unlock serious autonomy and allow you to scale with agentic workloads.

Canvas skills are how your team's runbooks and tribal knowledge become an active part of the investigation instead of a document someone has to remember to open. Pre-built skills cover the most common investigation patterns out of the box. Custom skills let you encode the specific context, thresholds, and decision logic your team has accumulated, so every auto-investigation starts with your best thinking already applied.

Honeycomb's Innovation Week: Observability for the Agent Era (May 12-14) For Day 1 of Innovation Week, Honeycomb co-founders Christine Yen and Charity Majors will share what it actually takes to understand and debug systems in the agent era, and what the best engineering teams are doing differently. A 3-Day Virtual Event for Teams Building the Future May 12: Get insights on how the best engineering teams are tackling the challenges of the agentic era.

Cribl's Product Manager David Cavuto walks through how quick and easy it is to get data ingested into Cribl Search's lakehouse engine.

Watch this video to see the re-imagined Canvas in action, where auto-investigation has already ranked your hypotheses before you open the tab, multiplayer agents build on each other's work in real time, and a custom skill encoding your team's own runbook can reprioritize the entire incident before you've had your morning coffee.

Watch this video to see Agent Timeline in action: one conversation ID, one view, every agent invocation, LLM call, tool call, and downstream trace, so you stop stitching tabs together and start finding the failure in seconds.

Watch a full replay of all sessions and demos on Day 2 of Honeycomb's Innovation Week.

Honeycomb's Innovation Week: Observability for the Agent Era (May 12-14) For Day 2 of Innovation Week, Honeycomb's product and engineering teams will take you inside the new capabilities purpose-built for the agent era. Expect live demos, real scenarios, and a hands-on look at what it means to own observability for the Agentic era, with AI in Honeycomb to observe AI in production. A 3-Day Virtual Event for Teams Building the Future May 12: Get insights on how the best engineering teams are tackling the challenges of the agentic era.

Watch a full replay of all keynotes on Day 1 of Honeycomb's Innovation Week.

Integrating security data with observability data provides a comprehensive view for better threat detection and response. Security observability helps connect the dots between seemingly innocent events that, when correlated, reveal complex attack patterns. SolarWinds security products integrate into observability self-hosted, including Security Event Manager for log data and event correlation, Access Rights Management for identifying potential attack vectors, configuration management for compliance monitoring, and Patch Manager for tracking critical updates.

Here are 3 YouTube title options plus a description optimized for technical/dev audiences: Same log pattern. Hundreds of useless groups. In this video, we show how to use Uptrace Grouping Rules to automatically turn noisy logs into structured, searchable data — without changing application code. You'll learn how to: Examples covered: Perfect for:#OpenTelemetry users, backend engineers, SREs, and anyone dealing with noisy logs.

Datadog has always been driven by a broader vision of helping teams understand and operate complex systems. In this session, you’ll hear from Michael Whetten, Product SVP, and Abrar Hussain, Senior Director, Product Management, as they share the latest updates across the Datadog product suite and discuss how that vision continues to shape the platform’s evolution and support the next generation of AI-driven applications.

Cheryl Nomanson, SolarWinds staff technical trainer, provides a comprehensive overview of SolarWinds architecture and scaling options for self-hosted deployments. She explains the centralized deployment model starting with a single SolarWinds server that handles polling, web console, and database connections. The presentation covers key scaling indicators including polling thresholds that warn users at 85% capacity and alert at 100%. She demonstrates how to add up to 100 polling engines per server and additional web servers to handle more concurrent users.

Understand the real difference between observability and monitoring — and why modern IT teams in 2026 need both. Monitoring tells you something is broken; observability explains why. See real examples, faster troubleshooting workflows, and how Motadata ObserveOps unifies both in one platform. Don’t forget to like, share, and subscribe for more IT insights.

Your SIEM and observability tools are expensive, and you're probably using them for things a data lake could handle for a fraction of the cost. Let's fix that.

The way you write code is changing, which means the way you observe your systems and respond to issues needs to change, too. Engineers today spend much of their day working via command line, as agentic tools like Cursor and Claude Code have become highly effective at handling many day-to-day engineering tasks. This greatly accelerates code generation, but it doesn't solve for the context switching that comes when you have to jump into another tool that's not part of this new, faster workflow.