Operations | Monitoring | ITSM | DevOps | Cloud

Continuous integration/continuous delivery (CI/CD) tools give developers the ability to automate the software development process. As soon as developers push code to git, your CI/CD system can build, test, stage, integration test, deploy, and scale. That’s fantastic! In this tutorial, we will look at CircleCI orbs and how they can support your CI/CD practice. We’ll look at how to use multiple orbs and how orbs can help with multi-builds for a variety of application types.

Business objectives for IT management have fundamentally shifted. Traditionally, organizations imposed a heavy-hand over the types of IT resources their workers employ and how they will use them. However, over the past decade or so, a growing awareness has supplanted these antiquated approaches suggesting technology should conform to the needs of the workers, rather than the other way around.

When the first supporting server-side infrastructure for Collapsed Reply Threads (CRT) shipped with Mattermost v5.29 (November 2020), it included an ominous release note: > This setting is enabled by default and may affect server performance. While performance concerns are possible with any new feature, most features don’t require significant architecture and data model changes. Most features don’t ship incrementally across 20 monthly releases. And most features – to their credit?

Running machine learning (ML) inference in Edge devices close to where the data is generated offers several important advantages over running inference remotely in the cloud. These include real-time processing, lower cost, the ability to work without connectivity and with increased privacy.

The Machine Learning team at Splunk has been hard at work over the last several months preparing for a few exciting launches at.conf22, held just a few weeks ago. Splunk customers want to leverage machine learning (ML) in their environments, but many aren’t sure how to use it, or even how to get started.

We announced Cribl Search in May, and customer reaction has been incredibly positive. We’ve heard for some time that organizations have data everywhere. They have data in their observability lakes, analytics tools, object stores, and at the edge. The big challenge facing enterprises is that existing search models require you to take all of this data that you don’t know is valuable or not, move it into one place, and then make decisions about whether this is valuable?

There is no escape from the need for patch management and updates. It is true that operating systems and software vendors are getting better, faster, and more efficient about how they make and deploy patches. However, for businesses, patch management remains a time-consuming necessity that has big impacts on security, compliance, and day-to-day operations for IT teams and the businesses they serve.

DevOps has made a hugely positive impact on the way organizations operate. In essence, it’s a way for different project stages and teams to work. Truth be told, when it comes to integrating various aspects of the IT R&D chain, it’s a godsend. On the other hand, Agile is about emphasizing continuous delivery in software development with a focus on iterative, incremental, and evolutionary development. Today, we’ll learn about DevOps vs.

The heart of any software development operation is infrastructure. This combination of virtual and physical assets ensures that the flow, storage, processing, and analysis of data remains efficient and as seamless as possible. When it comes to selecting a model for managing and deploying infrastructure, IT managers typically have two choices: infrastructure as code (IAC) or infrastructure as a service (IaaS).

Many think they know what network bandwidth is but conflate performance with capacity. This blog, among other things, will end that confusion. And for true IT experts, we’ll dive deep into the whys and wherefores of network bandwidth monitoring and optimization.