Operations | Monitoring | ITSM | DevOps | Cloud

datadog

How we cut Spark compute costs by 44% with agentic AI and Datadog Jobs Monitoring

Jun 1, 2026 By Charles Yu In Datadog
Spark jobs only get more expensive and harder to debug as they scale. It’s a problem we’ve run into ourselves. Our Referential Data Platform team builds and maintains the knowledge graph that maps relationships between customers’ observability entities. ServiceQueryEdge is at the center of that graph, mapping service entities to their associated metric and log queries.
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datadog

Migrate to Azure Managed Redis with Datadog and Eden

Jun 1, 2026 By Michael Cronk In Datadog
Azure Managed Redis is a Microsoft first-party, fully managed in-memory data store, replacing Azure Cache for Redis tiers. It includes Redis Enterprise features such as RediSearch for vector search and full-text search, in addition to RedisJSON, RedisTimeSeries, and Active Geo-Replication. As Azure Cache for Redis reaches end of life, more teams are planning migrations to Azure Managed Redis in search of better performance, lower cost, and modern capabilities for AI and real-time workloads.
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Cloud 66

Scaling Your App

Jun 1, 2026 By Kelley Schultz In Cloud 66
Every application starts the same way: One server. One database. One optimistic engineer saying: “We’ll scale later.” And honestly? That’s usually the right call. Premature scaling is how perfectly normal applications end up with: But eventually, growth happens. Traffic increases. Queries slow down. Deployments get riskier. Your infrastructure starts making unfamiliar noises. This is where scaling enters the picture. Not scaling for conference talks.
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logicmonitor

What a Forrester TEI study on Edwin AI actually tells IT leaders-and how to use it

Jun 1, 2026 By Margo Poda In LogicMonitor
This blog helps IT leaders use the Forrester Consulting TEI study as a practical framework for evaluating Edwin AI in their own environments. A Total Economic Impact study is useful for one, critical reason: it takes a broad technology claim and turns it into a financial and operational framework. That matters in AI for IT operations because the market is crowded with claims. Every platform says it reduces noise. Every platform says it improves efficiency. Every platform says it helps teams move faster.
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canonical

How Canonical Support solves hard Linux performance bugs - even in 12-year old code

Jun 1, 2026 By Lidia Luna Puerta In Canonical
Some support cases are straightforward. Others lead deep into legacy code, where a single logic bug can quietly turn a routine command into a major performance problem. This series looks at how Canonical Support and Sustaining Engineering work together to investigate, patch, and upstream difficult issues that standard troubleshooting alone cannot solve.
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iOPEX

Built by ServiceNow, Extended by iOPEX: The Outcome-driven Co-Delivery Model for Agentic Transformation

Jun 1, 2026 By iOPEX Technologies , In iOPEX
ServiceNow's internal IT operations now resolve more than 90% of employee requests through autonomous agents. The platform that demonstrated this in Las Vegas earlier this month is the same platform sitting in your environment right now. So why isn't your operation running the same way? This is the question every CIO should have walked out of Knowledge 2026 with.
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motadata

15 DevOps Metrics Every Engineering Team Should Track in 2026

Jun 1, 2026 By Written by In Motadata
Software moves from code to production more quickly today, but it is still difficult to tell whether delivery is actually improving or just becoming more active. Most teams rely on dashboards filled with metrics like deployments, uptime, failures, and tickets. The numbers are available, but the meaning behind them is often unclear. DevOps metrics become useful only when grouped into clear categories: DORA metrics cover only delivery speed and stability, which is just part of the picture.
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signl4

How to Build Escalations That Actually Work

Jun 1, 2026 By SIGNL4 In SIGNL4
Most IT teams already know when something breaks. The real problem is making sure the right person responds fast enough. A server goes down. A customer-facing application crashes. A security alert triggers after hours. The monitoring system sends the notification. But nobody responds. The alert gets buried in Slack. The on-call engineer misses the push notification. The wrong person is scheduled. Everyone assumes somebody else is handling it. That is how small incidents become expensive outages.
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From Cleanup to Animation in One Workspace: Redefining the Editing Loop

From Cleanup to Animation in One Workspace: Redefining the Editing Loop

Jun 1, 2026 By OpsMatters In OpsMatters
For the past several months, I have been watching a pattern emerge in how people actually use AI image tools. The pattern is not about any single feature. It is about how often a task that starts as a simple cleanup request evolves into something entirely different. A user uploads a product shot to remove a stray reflection. Then they wonder what the same image would look like with a different background. Then they think about turning it into a short social video. Each step is logical, but traditional workflows treat each step as a separate job requiring a separate tool.
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