Operations | Monitoring | ITSM | DevOps | Cloud

Demand for DevSecOps products has been growing strongly, as more companies realize the importance of integrating security into their DevOps pipelines. However, IT and DevOps pros who dive into the DevSecOps market looking for options quickly realize that the number of DevSecOps tools and frameworks is vast and confusing.

When you choose a product, you’re hiring it to do a job. You’ve put out the“Help Wanted” sign for DevOps, and choosing between two well-qualified prospects is high stakes. The hire you make can ensure the enterprise swiftly rises — or sinks. With JFrog and GitHub, you have two of the best candidates. Now judge which is the best fit. Beyond the puzzle of competing features, which one knows how best to get the job done?

NeuraLegion’s VP Oliver Moradov takes us through how you can use JFrog and NeuraLegion to automate AppSec testing in your pipelines. The days of long release cycles are well and truly behind us — it is simply not feasible in our agile development world, with developers delivering software and more features at an unprecedented scale and speed. With DevOps, we have multiple development teams running multiple concurrent builds, which is great, but security testing has not kept up.

Today’s enterprises are pushing forward with their digital transformation initiatives to meet customer and market demand. The latest CNCF survey reports that 91% of companies are running Kubernetes and 81% of those companies are running Kubernetes in production. That’s up from 58% in 2018, and the numbers continue to ramp up quickly. There’s several approaches to how enterprises are thinking about adoption and their deployment and management of Kubernetes. I sat down with Lauren E.

This is an example machine learning image recognition stack using AWS Lambda Container Images. Lambda container images can include more source assets than traditional ZIP packages (10 GB vs 250 MB image sizes), allowing for larger ML models to be used. This example contains an AWS Lambda function that uses the Open Images Dataset TensorFlow model to detect objects in an image.

When reading about Chaos Engineering, you’ll likely hear the terms “fault injection” or “failure injection.” As the name suggests, fault injection is a technique for deliberately introducing stress or failure into a system in order to see how the system responds. But what exactly does this mean, and how does this relate to Chaos Engineering?

A decade ago, DevOps teams were slow, lumbering behemoths with little automation and lots of manual review processes. As explained in the 2020 State of DevOps Report, new software releases were rare but required all hands on deck. Now, DevOps teams embrace Agile workflows and automation. They release often, with relatively few changes. High-quality DevOps change management is no longer a nice-to-have, it’s a must. For a lot of DevOps teams, this is easier said than done.

Tracking Apache server performance is important to avoid future problems. Hence, what is Apache? Apache is one of the most popular and widely used web servers. As an open source cross platform HTTP server, it can be run in a Linux, Unix, or Windows environment. Stable modular Apache architecture can be configured for multiple needs and it’s crucial to provide seamless and efficient server functionality.

Many organizations use out-of-date architecture patterns for developing relational databases which are a pain to manage, slow developers down, and limit testing and innovation. It’s time to modernize these environments and improve standards for database development. In the years I worked as a consultant specializing in SQL Server administration and tuning, I frequently fielded questions from customers about how to best manage databases in development and test environments.