When you’re planning an application performance monitoring (APM) strategy, collecting metrics from storage services like Amazon S3 may not seem like a priority. After all, part of the point of object storage is that applications can read and write from storage buckets seamlessly, with minimal configuration and overhead.

Application monitoring is a well-established discipline that dates back decades and remains a pillar of software management strategies today. However, as software environments and architectures have evolved, monitoring techniques have needed to evolve along with them. That’s why many teams today rely on distributed tracing to glean insights that they can’t gather from application monitoring alone. Distributed tracing provides a deeper level of visibility into complex distributed environments than application monitoring can achieve.

If your team falls into the majority of organizations that use NGINX – which remains the world’s most popular Web server – to host websites and Web applications, monitoring NGINX usage, performance, and transactions is critical for maintaining a positive end-user experience.

Microservices and serverless are both important topics in the world of cloud-native computing. Yet, although serverless functions and microservices architectures often go hand-in-hand, they’re distinct technologies that fill different roles in modern software environments.

Monitoring Redis, the open source in-memory data platform, is complicated enough when you are hosting your Redis instance on just a single server. It gets even more complex when you build a Redis cluster that consists of multiple nodes and distribute your data across them.

One of the more delicate debates in the DevOps world is what observability has to do with monitoring. Is observability just a trendy buzzword that means the same thing as monitoring? Is observability an improved version of monitoring? Are monitoring and observability different types of processes that solve different problems? The answer to those questions depends in part on your perspective. Let’s take a look at the different ways of thinking about observability and monitoring, and what they have to do with each other.

Aurora, a hosted relational database service available on the Amazon cloud, is a popular solution for teams that want to be able to work with tooling that is compatible with MySQL and PostgreSQL without running an actual MySQL or PostgreSQL database.

Amazon Redshift is a cloud-based data warehousing solution that makes it easy to collect and analyze large quantities of data within the cloud. Cloud data warehouse services like Redshift can remove some of the performance and availability pain-points associated with on-premises data warehousing, but they are not a silver bullet. Getting the most out of Redshift requires carefully monitoring Redshift clusters in order to identify stability issues and performance bottlenecks.

For decades, Microsoft SQL has been a leading relational database solution within Windows-based environments. The extension of Microsoft SQL support to Linux servers in 2017 made the platform even more popular. There’s a good chance that, no matter which types of infrastructure or servers you manage, there are Microsoft SQL databases residing somewhere on them.

Amazon EC2 offers a flexible and convenient way to run virtual machines in the cloud. With dozens of EC2 instance types available, as well as multiple pricing options, it’s easy to use EC2 to configure the best cloud-based virtual machines for your needs and budget.

At its core, DevOps is a set of principles. There are certain types of tools and processes that reinforce those principles and put them into practice. But the tools and processes are just a means to an end. The end itself is DevOps principles.

The most common way to work with JFrog Artifactory is through the browser-based interface. But if you’re using Artifactory in conjunction with npm, the Node.js package manager, there are also handy interfaces available through both the npm command line and the Artifactory CLI.

Security was once the bane of Docker containers. As recently as 2014, bloggers were writing things like "using containers for security isolation is not a good idea" and that Docker is not safe "in absolute terms."