Operations | Monitoring | ITSM | DevOps | Cloud

Explore how to leverage observability insights to reduce metric costs with Span Observability. Led by Tomasz Romaniuk and Martyna Karbownik from Cisco, this tech talk covers the significance of metric time series in impacting cardinality and consequently driving metric costs. After a theoretical introduction, the presentation dives into practical real-life use cases demonstrating how to effectively utilize available tools for cost reduction. The session concludes with an overview of future developments and a Q&A segment for audience inquiries.

Charlie Huggard traces his path from teddy bear–loving customer, to Splunk Trust member, to technical leader at Splunk. Along the way, he reflects on the community that helped him grow and the meaningful values that make Splunk special.

It's time to go from reactive firefighting to predictive prevention. Learn how the Time Series Foundation Models can forecast system failures before they cascade.

Small language models are cheaper, run privately on your own hardware, and often beat the big models on focused tasks like sorting emails or summarizing transcripts — and they're perfect for checking a large model's work. The key is knowing when to use which.

Participants are welcomed to a session titled "Observability Simplified," focusing on user experience, application performance, and network behavior. This second part of a three-part series highlights how the Splunk Observability Cloud and Cisco ThousandEyes can create a unified view of applications, infrastructure, and network performance. Key discussions include addressing siloed troubleshooting, enhancing visibility, and a live demo showcasing how to identify network issues affecting application performance. Attendees are encouraged to participate in the Q&A and are reminded that the session will be recorded for future reference.

AI applications can behave unpredictably, potentially leading to errors such as hallucinations or data leaks, even when classic monitoring indicates a successful response. To effectively monitor AI systems, four key areas should be focused on. Implementing these pillars can enhance trust in AI deployments, help manage costs, and identify safety issues before they impact users.

A semantic-convention rename (http.method → http.request.method) can silently break your RED metrics — no errors, just gaps in dashboards and alerts. The OpenTelemetry Collector's schema processor fixes it: put it first in your pipeline and it normalizes attribute names no matter what each service emits. Migration mode writes BOTH the old and new names, so you get zero-downtime upgrades while queries keep working.

This walk-through shows how Splunk's Cloud can discover schema and partition keys for Microsoft Azure Blob Storage datasets and create searchable Splunk managed tables. Once the data is mapped, analysts can use Splunk Federated Search to query Azure Blob data where it lives, bringing cloud-resident logs into security, observability, and operational work-flows without re-ingesting the data.

This walk-through shows how Splunk's Cloud can discover schema and partition keys for Microsoft Azure Blob Storage datasets and create searchable Splunk managed tables. Once the data is mapped, analysts can use Splunk Federated Search to query Azure Blob data where it lives, bringing cloud-resident logs into security, observability, and operational work-flows without re-ingesting the data.

This walk-through shows how Splunk Cloud can search AWS S3 data through an Apache Iceberg REST catalog backed by Nessie. Learn how Iceberg table metadata, S3 storage, and Splunk Federated Search work together so analysts can query historical security data where it lives without reingesting it into Splunk.