Operations | Monitoring | ITSM | DevOps | Cloud

This video provides a step-by-step walkthrough for configuring monitoring for Java applications running on Windows using SolarWinds Observability. The demonstration covers the complete process—from adding a new service to instrumenting the application with the Java APM library and verifying connectivity. Topics covered in this video include: This guide is designed for developers, DevOps engineers, and system administrators who need to instrument Java applications on Windows for performance monitoring, distributed tracing, and full-stack observability.

Datadog Data Observability provides a complete solution with quality checks (e.g., volume, row changes, freshness), custom SQL-based monitors, anomaly detection, column-level lineage across systems like Snowflake and Tableau, full pipeline visibility, and targeted alerts when data issues arise.

Cribl Search gives you one lens across all your telemetry data no matter where it lives. Instead of forcing teams to move data into one system or jump between tools, you get a familiar pipe based query experience with dashboarding and alerting built in. Storage and query processing stay separate so you decide where your data lives while your users get fast, simple access in one place.

Disconnected pipelines. Unknown data sources. Costs that do not add up. Many teams struggle to answer a simple question. What data do we have and where is it going? In this clip, a Cribl customer explains how bringing all telemetry data together changed everything. With Cribl, their team can finally see what they collect, where it flows, and what it costs. That clarity unlocked smarter reduction, better routing decisions, and major optimization across security and observability workflows.

Customers asked for a simpler way to store and access telemetry data, and Cribl delivered. First came Cribl Lake. Cost effective data storage, flexible access, and identity based authorization instead of infrastructure based access rules. A simple way to retain data at rest and run slow, inexpensive analytics when needed. But the story did not end there.