Operations | Monitoring | ITSM | DevOps | Cloud

When you first start coding in Go, you quickly learn how error handling in the language differs from error handling in languages such as Java, Python, JavaScript, or Ruby. In those languages, throwing an exception automatically generates a stack trace. Go, by contrast, provides no built-in error tracing to reveal an error’s origin.

Data Build Tool (dbt) is an open source analytics engineering framework that enables teams to transform raw data that has been loaded into a warehouse like Snowflake, BigQuery, Redshift, or Databricks using SQL-based workflows. dbt is available in two main forms: dbt Core, the free and open source CLI tool, and dbt Cloud, a managed platform that adds scheduling, UI support, collaboration tools, and native integrations.

As cloud infrastructure becomes more dynamic and distributed, DevOps and security teams need to quickly detect risks and understand their context: where those risks live, how critical they are, and how to respond effectively. By surfacing misconfigurations, vulnerabilities, sensitive data risks, and identity threats directly on a real-time diagram of your infrastructure, Cloudcraft helps teams identify, prioritize, and remediate security issues at scale.

In the August episode of This Month in Datadog, Jeremy shares how you can make more informed cloud cost decisions, gain insights into your LiteLLM-powered applications, and secure Kubernetes infrastructure with Datadog Workload Protection. Later in the episode, Danny puts the spotlight on Datadog Kubernetes Autoscaling, which helps you deliver cost savings without sacrificing performance.

As organizations increasingly adopt multi-cloud strategies, identifying areas to reduce cloud spend has become highly complex and time consuming. While there are many reasons that organizations choose to run their infrastructure in a multi-cloud environment, many do so to comply with regional data requirements, take advantage of best-of-breed offerings, or avoid vendor lock-in.

When developing software, the longer you intend to keep a system around, the more important it becomes to prioritize its code quality. But as more organizations move toward microservice architectures and adopt agentic AI and LLMs into their development workflows, many engineering teams have increased their emphasis on accelerating developer velocity, often at the expense of code quality. This can often result in code that fails to meet standards for performance, reliability, and security.

Kubernetes v1.34, which is scheduled for release August 27, 2025, focuses on improved scheduler visibility, deeper life cycle observability, and enhanced resource management. As always, the list of changes and improvements in the official changelog is extensive, and cluster operators may be wondering which changes are most important. If you're operating a monitoring platform or depend on deep Kubernetes observability, here's how a number of new features will affect your workflows.

In the early stages of building a system, a few well-placed dashboards and monitors can provide sufficient visibility into service health and performance. However, as infrastructure scales and teams grow, so does the complexity of the monitoring landscape. In organizations where individual teams manage their own services but rely on a central platform or observability team for tooling and guidance, this complexity can quickly multiply.

As modern infrastructure becomes increasingly distributed across on-premises data centers, multi-cloud environments, ISPs, and remote offices, understanding how traffic flows across your network is critical to delivering reliable performance and great user experiences. But pinpointing the source of network slowdowns remains one of the most persistent challenges for operations, network, and IT teams.

Modern application development is evolving rapidly, with serverless containers and microservices becoming the standard for scalable, resilient architectures. Azure Container Apps is at the forefront of this movement, enabling developers to deploy containerized applications without having to manage infrastructure.