Operations | Monitoring | ITSM | DevOps | Cloud

With the release of SQL Sentry 2023.3, I look back at the past 18 years I have worked with the product to admire where it is today versus then. It’s been an incredible experience with a lot of moving parts, but no matter what market forces or acquisitions happen along the way, the core intention of “Improving the lives of Microsoft DBAs everywhere” has stayed the same. This is a two-part blog post: below, I’ll talk about our history and where we’ve come from.

The advent of the data mesh has changed the way organizations manage and utilize data. It’s not expected to go away anytime soon. Data mesh, in short, is a popular approach to data management in the enterprise. Its primary goal is to empower teams, especially cross-functional ones. In this article, we'll delve into what data mesh is and explore its key principles. We'll also discuss some benefits of implementing this modern data mesh approach in your organization.

Enterprise IT is just a different animal. Whether it’s operating at scale, undertaking massive migrations, working across scores of teams, or addressing tight security requirements, engineers at these organizations can face different obstacles than their counterparts at smaller organizations and startups.

As connected as the world is today, it can feel quite disconnected, especially in IT. Individual teams use various tools, each with specialized interfaces and dedicated dashboards, to present or interpret data each the specific functional team needs.

In recent years, microservices have emerged as a popular architectural pattern. Although these self-contained services offer greater flexibility, scalability, and maintainability compared to monolithic applications, they can be difficult to manage without dedicated tools. Kubernetes, a scalable platform for orchestrating containerized applications, can help navigate your microservices.

The grocery sector was arguably hit as hard by the pandemic as any and is still dealing with the most persistent hangover—namely ongoing supply shortages, inventory imbalances, and excessive spoilage and waste.

Data visualization is a way to make sense of the vast amount of information generated in the digital world. By converting raw data into a more understandable format, such as charts, graphs, and maps, it enables humans to see patterns, trends, and insights more quickly and easily. This helps in better decision making, strategic planning, and problem-solving. Visualization and understanding data are critical in platform-as-a-service (PaaS) offerings like Heroku.

Internet protocols are the lifeblood of internet communication, powering important connections between servers, clients, and networking devices. These rules and standards also determine how data traverses the web. Without these protocols, internet traffic as we know it would be severely fragmented or even grind to a screeching halt. And without evolving protocol development, the web couldn’t properly support the applications driving massive traffic volumes worldwide (or vice versa).

In this livestream, Cribl’s Ahmed Kira and I explore the challenges of scaling your Cribl Stream architecture to accommodate a large number of agents, providing valuable insights on what you need to consider when expanding your Cribl Stream deployment. Managing data flows from a high volume of agents presents a unique set of challenges that need to be addressed.

If you’re an IT / EUC professional looking to accelerate sustainable IT practices, it’s imperative to put employees at the center of your strategy. Driving sustainable practices and carbon reduction is a collective and long-term effort that requires behavioral change and impacts individual digital workspaces, so engaging employees is key.