Operations | Monitoring | ITSM | DevOps | Cloud

When all your IT systems, your apps and software, and your people are spread out, you need a way to see what’s happening in all these minute and separate interactions. That’s exactly what distributed tracing does. Distributed tracing is a way to tracking requests in applications and how those requests move from users and frontend devices through to backend services and databases.

Simply put, GitOps applies DevOps practices like version control and CI/CD to infrastructure management. The name GitOps, coined by Weavewerks, is from Git, an open-source version control system that allows teams to manage infrastructure using the same tools and orchestration processes they use for software development.

What are microservices? Microservices are a software approach that creates applications as a loose coupling of specific services or functions, rather than as a single, “monolithic” program. A microservice architecture increases the speed and reliability with which large, complex applications are delivered. What makes a service a microservice? Microservices are defined not by how they’re coded, but by how they fit into a broader system or solution.

Maybe as a kid, you pretended to have a magic wand. You would say something like, “Show me the answer to this long division question” then wave your magic wand and wait for the answer. Sadly, mine never seemed to work – for math questions or to make magical snacks appear. Now, imagine if you had a magic wand for your application stack where you could ask it a question about your data and it would give you immediate insights.

Observability first emerged as a focal point of interest in the DevOps community in the 2017 time frame. Aware that business was demanding highly adaptable digital environments, DevOps professionals realised that high adaptability required a new approach to IT architecture. Whereas historically, digital stacks were monolithic or, at best, coarsely grained, the new stacks would have to be highly modular, dynamic, ephemeral at the component level, and spread over multiple cloud-based services.

With the rapid adoption of cloud, distributed systems and microservices are standard, resulting in increasingly complex environments. Once straightforward troubleshooting workflows have become chaotic, frustrating, and time-consuming. When something breaks, multiple teams are called to the table to prove they’re “not it”; each with their singular view of the problem.

What do the gauges on your car, the jumbotron at a stadium, and Splunk Observability Cloud all have in common? They all show you the important information to know how something is performing. We all know what to look for in the first two examples.

Simply put, value stream management (VSM) is the practice of measuring and improving the flow of business value created by an organization’s software delivery efforts.By monitoring the software delivery life cycle end-to-end, organizations can better identify processes that add value and eliminate those that create waste to optimize the flow of work. Ultimately, this enables teams to move away from activities that don’t directly contribute to customer value and focus more on those that do.

A data platform is a comprehensive end-to-end solution for all your data. A true data platform can ingest, process, analyze and present data generated by all the systems and infrastructures within your organization. In this topic, there’s a lot of things to understand and consider. So, let’s take a deep look at data platforms, including the definition and related terms, the benefits and use cases, and how to start building your data strategy.

Businesses today rely on analytics and insights derived from different data types for gaining competitive advantages. These data often come from different sources and in different formats. Without a unified solution, aggregating those data and performing analytics tasks is challenging. ELT has been invented to solve the complexities associated with processing data from multiple sources while retaining the raw data as it is.