Operations | Monitoring | ITSM | DevOps | Cloud

According to the 2024 State of SaaSOps report, organizations now use an average of 112 SaaS applications. That’s 112 potential points of failure. Manually checking or subscribing to each of those status pages is not scalable. Even small teams often rely on 30+ services spanning infrastructure, communication, payments, and security. A status page aggregator like StatusGator consolidates service statuses from hundreds or even thousands of providers into a single, unified view.

As a data platform engineer, you’re tasked with running complex workloads—Apache Spark jobs, AI/ML pipelines, batch ETL—across dynamic Kubernetes environments. Performance matters. Time spent tuning matters. And so does cost. But if you’re still relying on manual resource tuning to optimize your workloads, you’re playing a losing game. Sure, you can tweak CPU and memory requests by hand. You can comb through Prometheus metrics, look at job logs, estimate peaks.

If you want a secure, efficient, and compliant network, network configuration management is a must. Whether managing a small network or being responsible for a large enterprise system, having the right solution can make all the difference. Network configuration management tools provide valuable insights into devices on your network, and they can help quickly restore previous configurations in the event of a failure, misconfiguration, or security incident. What is network configuration management?

By centralizing your mission-critical logs, metrics, traces, and events from all of your systems into one platform, Sumo Logic enables teams across development, security, and operations to operate from a single source of truth. While this unified approach is crucial for fast issue identification and minimizing downtime from infrastructure failures or security breaches, not everyone on your team needs access to every bit of data.

As your applications scale across cloud and SaaS providers, allocating costs and optimizing workloads become increasingly important—and challenging. Without access to cost data in their daily workflows, engineering teams can’t easily understand the cost of their resources and identify where they can reduce their spend. And while FinOps teams have access to cost data, they often review this information in silos.

Delivering software quickly and reliably is the main focus of modern DevOps. But to improve your delivery performance, you need to understand it, and that starts with measurement. Teams primarily measure performance in this area by using DORA metrics—deployment frequency, change lead time, change failure rate, and time to restore service*. These metrics help teams understand trends in their software delivery practices in quantifiable terms that they can track and improve over time.

If you’re a Datadog customer, you’re likely using our platform to gain visibility into your infrastructure and applications and to troubleshoot using logs, metrics, and traces when issues arise. To support these efforts, you’ll want access to the most granular telemetry signals and intuitive workflows that streamline your investigation.

Starting on Aug. 1, 2025, AWS will bill for the initialization (INIT) phase of Lambda functions, bringing a key change to how you are charged for serverless workloads. This billing update will impact functions using managed runtimes with ZIP archive packaging, which previously excluded the INIT phase from the billed duration. For teams that rely heavily on AWS Lambda, this is a small but significant change. The INIT phase, while short, could introduce costs that were previously invisible.

Rancher Continuous Delivery (known as Fleet) can be used in a workflow to deploy applications to many clusters. With its GitOps support, it enables downstream clusters to pull updates from a Git repository. We know of users that monitor several hundred Git repositories and deploy to a thousand clusters. To make this scale possible, several intermediate steps are necessary. First, the application is converted into separate bundles, which are then targeted at clusters.

Mobile apps are a little different from services run on servers. You build your mobile app, you ship it off to the world, and then it gets run by the end user on their own machine. If your app is running poorly on some percentage of users’ devices, you may never know. That’s where observability comes in. There are certain important metrics that every mobile app has in common.