In this previous article we learned to create our own Docker images to test and distribute our applications regardless of their dependencies. As we know, Docker is a wonderful tool for collaborative programming work, allowing to work in a virtually identical environment regardless of the operating system you are in.

Since StatsD was introduced by Etsy in 2011, it has become a mainstay of infrastructure monitoring. But as dev and ops teams rely more and more on containerized microservices, they have pushed the limits of StatsD’s design. One major shortcoming is that StatsD has no built-in support for tagging your metrics with key-value pairs.

At Blameless, we drink our own champagne, and aim to adopt a mindset of continuous learning to foster resilience. We believe that the adoption of SRE practices is one of the best ways to get there. Like most organizations, our early efforts to implement SRE were imperfect. However, through hard work, teamwork, and investing in what we believe is the most important feature (reliability), we have made significant changes to how we do SRE. And we’re getting better at it every day.

If you have ever used a search bar on a website, you've probably used Elasticsearch. Elasticsearch is an open-source search and analytics engine used for full-text search as well as analyzing logs and metrics. It allows websites to use autocomplete in text fields, search suggestions, location or geospatial search. Tons of companies use Elasticsearch, including Nike, SportsEngine, Autodesk, and Expedia.

There’s no one-size-fits-all migration path for moving legacy applications to the cloud. These applications typically reside on either physical servers, virtual machines or on premises. While the goal is generally to rearchitect or redesign an application to leverage cloud-native services, it’s not always the answer.

When evaluating Kubernetes providers, you’ll quickly see that they ALL support upgrades. But here’s a little dirty secret, no independent Kubernetes multi-cloud, multi-cluster platform supports rolling updates. Instead, you’ll need to deploy a different cluster and replicate your app to ensure service delivery while the original cluster is updated. That process is cumbersome and requires far too many resources.

In the Data Age 2025 report, worldwide data is expected to grow 61% to 175 zettabytes by 2025. The enterprise sector, in particular, generates more than 30% each year. To be ready for a digital future, consider the scaling strategy of data infrastructure beforehand. Scale-up and scale-out are the main ways to add capacity to your infrastructure.

The seamless scaling and headache-free reliability of a serverless application architecture has become compelling to a broad community of cloud specialists. But for those who have yet to become converts, a specific issue related to service startup latency—Cold Starts—has been one of the cited key objections. Fortunately, the serverless marketplace is maturing.

Two years ago, I wrote a similar post on Microsoft Flow: How to pass the SQL data table results in a Markdown-formatted table into an Email notification or Approval Requests. It is still quite up to date, i.e., it is an excellent solution that you can still use today. The only problem with that article is that I was using “send an email” action in Power Automate Outlook connector that is currently deprecated.

In a previous article, we examined service meshes in detail. Briefly, a service mesh takes care of network functionality for the applications running on your platform. As Kubernetes has matured as a technology, service meshes have become a hot topic, with various products being developed to solve the challenges associated with areas like traffic management, security, and observability. This article will compare three service meshes.