Operations | Monitoring | ITSM | DevOps | Cloud

As organizations continue to heavily invest in AI and build more agentic workflows, their telemetry data volumes can surge quickly, and the associated costs can become unpredictable. To regain control of their data, many AI-forward teams are turning to high-throughput, low-latency pipelines to collect and route data to tools such as OpenTelemetry (OTel) and ClickHouse. But these self-hosted solutions come with drawbacks.

Single Step Instrumentation (SSI) simplifies Datadog Application Performance Monitoring (APM) by automatically discovering and instrumenting services across a host. For many teams, SSI is the ideal starting point because it helps them achieve full visibility with minimal setup. However, as environments grow, teams often want more control over which services get traced. Auxiliary workloads such as batch jobs and cron tasks might not require distributed tracing.

If you’re building LLM-powered applications and agents, you’ve probably asked yourself: “How do I know if my changes actually made things better?” You can tweak prompts, adjust temperature settings, or try different models, but it’s not always easy to validate whether version B’s response is better than version A’s. Most teams fly blind in preproduction and rely on user feedback to see how well their application works in the real world.

Boomi is an Integration Platform as a Service (iPaaS) used by thousands of organizations to connect applications, data, and workflows across cloud and on-premises environments. Business-critical processes, from order fulfillment pipelines to customer data synchronization, depend on Boomi Atoms and Molecules running reliably.

When engineers investigate SQL queries, they normally think of index scans as a fast and efficient step in the query’s execution plan. When executed correctly, they fetch only the relevant rows from your table as opposed to sequential scans that read the entire table, reducing latency and query costs. However, just because an execution plan uses an index scan doesn’t mean that the scan is fast or performant.

Platform engineering teams have access to hundreds of metrics, yet over 40% of platform initiatives cannot demonstrate measurable value within the first year. Teams that cannot quantify their impact fail to obtain executive sponsorship, risk being defunded, and ultimately, face deprecation. To accurately calculate a platform’s ROI, platform engineering teams need to differentiate between signals that measure platform effectiveness and those that should be used solely for investigative purposes.

Recorded Future provides real-time threat intelligence about indicators of compromise (IOCs), including malicious IP addresses, domains, and vulnerabilities. It also adds context on threat actors and campaigns to help security teams understand which signals represent real risk and prioritize their responses accordingly.

This guest blog post is by Tohn Furutani, SRE Engineer at NTT DATA. Over the past year, the conversation around generative AI has shifted from single-shot use cases—such as summarization, Q&A, and chat interfaces—to agentic AI systems that can make decisions based on context, plan multistep actions, invoke tools, and adapt as conditions change.

Datadog Session Replay heatmaps track where users click, scroll, and engage across your web pages. Each heatmap is overlaid on a screenshot of the page, and that background determines what you can actually analyze. But getting the right screenshot can be tricky. Many UI states are dynamic, rare, or simply impossible to capture from replays, so heatmaps can end up showing the wrong view.

ClickHouse is widely used for large-scale analytics, but once it is running in production, it can be difficult to understand how query activity translates into resource usage. Engineers investigating performance issues often struggle to determine which queries consume the most memory, run most frequently, or cause spikes in load. In practice, engineers are left querying system.query_log, tailing server logs, and piecing together information after an incident.