Since the data lake concept emerged more than a decade ago, data lakes have been pitched as the solution to many of the woes surrounding traditional data management solutions, like databases and data warehouses. Data lakes, we have been told, are more scalable, better able to accommodate widely varying types of data, cheaper to build and so on. Much of that is true, at least theoretically.

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.

This blog demonstrates how to create “inputs” to Cribl Search dashboards. An Input is a control widget that we can add to our Dashboards to control how they execute. They allow the user to supply a range of inputs to customize one or many of the Searches in each of the panels on a given dashboard. Currently, there are four types of inputs: a time picker, a dropdown, a string, and a number. This blog shows how to create all four types of Inputs on a dashboard using built-in sample data.

When things go wrong, we’d all love the ability to go back in time, return things to the way they were, and fix whatever issues pop up at the start so they never happen in the first place. This is no different when maintaining complex microservices-based architectures. With any complex system, things are bound to go wrong from time to time.

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.

Empty spaces, what are we searching for? Abandon queries, but do you know the score? On and on, Does anybody know what we are looking for? … Inspired by “The Show Must Go On”, Queen. Since we launched Cribl Search back in late 2022, we’ve been hard at work on adding features and functionality that continue to empower data engineers to do more with their data without needing to collect it first.