Operations | Monitoring | ITSM | DevOps | Cloud

I’m genuinely excited about what we're rolling out with Copilot Editor, an update to our AI that’s truly packed with new capabilities designed to help you automate pipeline development. You can read about these capabilities here. I wanted to take a moment to share our thinking on a core principle that guides how we build, especially regarding the impactful, and sometimes daunting, world of generative AI.

Ever spent a week wrangling log pipelines just to get your SIEM to stop screaming about missing fields? Wasted way too much time stripping out noisy events and reformatting data for analytics? You’re not the only one. If you work in Security or ITOps, you know the pain: every new data source means another round of schema headaches, more manual mapping, endless field transformations, and a quick prayer that you didn’t break something critical (or let in a flood of junk events).

If you’re a Splunk user relying on Dynamic Data Self Storage (DDSS) or Active Archive (DDAA), you’re likely familiar with this frustrating loop.

Have you heard that Search Packs are now officially generally available?

Cribl Packs have always provided a powerful way to package and share configurations across Cribl Stream environments. From pipelines to lookups, knowledge objects to functions—Packs make telemetry pipelines simple and portable. Now, we’re excited to announce a game changing expansion: Sources and Destinations can now be included in Cribl Packs!

Cribl recently introduced Lakehouse, a powerful new feature within Cribl Lake that enables fast queries on the freshest data. But it’s so much more than just speedy searches. Lakehouse redefines how organizations collect, store, manage, and analyze telemetry data at scale, ensuring a future-proofed, cost-efficient, and flexible approach to data management.

Cribl’s powerful data processing engine is designed specifically for IT and Security teams, enabling organizations to take control of their ever-growing data volumes. By simplifying the management, processing, and analysis of telemetry data, such as logs, metrics, and traces, generated across complex digital environments. This empowers organizations with the choice, control, and flexibility to manage and analyze data, allowing them to adapt to evolving needs and strategies.

At least once a week, a team reaches out to discuss migrating from an established SIEM or analysis platform. This major decision is influenced by several compelling factors, which can create significant work for engineering teams and pose risks to the business. The cost of switching to a new platform, often referred to as displacement costs, can be substantial.

Organizations today are grappling with an explosion of telemetry data growth as cloud adoption accelerates, digital infrastructures expands, and operational complexity increases. More data creates more challenges for IT and security teams as they struggle to separate signal from noise while maintaining compliance and efficiency within constrained budgets. It often feels like being caught in the deep end of a wave pool without a floatie, with each new data source sending another wave crashing down.

Data is the lifeblood of every business, but most of it is underutilized, buried under high costs and complex schema management. Simply funneling data into an analysis platform or cheap storage isn’t enough. Data deserves better.