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Enterprise Policy Management (EPM) is a programmable policy-as-code layer that controls the security, compliance, and flow of artifacts across the software supply chain. Teams can codify rules once and apply them continuously across repositories. With Cloudsmith’s platform, organizations extend policy enforcement across teams, environments, and geographies without introducing friction, including the open source packages that the chain depends on.

Cloudsmith built Enterprise Policy Management (EPM) on Open Policy Agent (OPA) and uses Rego to define policies as code. These policies control how packages move through your systems. They're versioned, reviewable, and enforceable. EPM is in early release, but it already draws on extensive metadata Cloudsmith collects from your artifacts: format, version, tags, license, vulnerability, malware scan results, and digital signatures.

In cloud environments, logs are often spread across numerous services, making it difficult to track down issues or gather meaningful insights. For AWS users, this challenge can become especially time-consuming. Centralized logging in AWS helps by bringing all your logs into a single platform, making management and analysis easier.

In modern microservices architectures, container observability is crucial for maintaining reliability and performance. It helps teams detect issues early and optimize distributed systems. This guide will walk you through the essentials of container observability, including advanced techniques and troubleshooting strategies to ensure your containerized applications run smoothly.

One tracks what happened, who did it, and when it happened. The other monitors how your systems are performing so you can see why and do something about it. Knowing the difference between CloudTrail vs. CloudWatch isn’t just helpful for engineers. It’s essential for finance and leadership teams, too. That’s because the two services can quietly rack up costs in the background.

Implementing robust data governance in microservices architectures presents unique challenges and opportunities. As organizations decompose monolithic applications into distributed services, traditional centralized data management approaches no longer suffice. Each microservice may manage its own data store, creating potential inconsistencies, compliance risks, and security challenges.

Frameworks evolve, libraries change, and APIs get rewritten. But your ORM decision? That one sticks—it shapes your architecture, guides how your team writes queries, and affects how painful refactors become later on. In.NET, this choice often narrows to Dapper and NHibernate—two trusted tools with fundamentally different approaches. NHibernate offers deep abstraction, rich mappings, and built-in caching, while Dapper gives you raw speed, total SQL control, and zero overhead.

In.NET, data access has evolved, but finding the right tool still comes down to control vs. convenience. You have to decide: do you prefer to write every query or move faster with something easier to maintain? Can you manage the boilerplate, or would you rather work with leaner syntax? For many.NET developers, the answers point to one of two popular tools—ADO.NET or Dapper. ADO.NET gives you complete control but with boilerplate and manual overhead.

When I first started writing Go software a little over a decade ago, one of the features I found particularly intriguing was the ability to build statically-linked binaries for multiple operating systems and architectures without a lot of headache. This build toolchain feature is widely relied upon by nearly all Go developers, especially when needing to build multi-arch container images destined to be run in a Kubernetes cluster consisting of amd64 and/or arm64 nodes.