Operations | Monitoring | ITSM | DevOps | Cloud

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.

With an increasing number of organizations migrating their applications and workloads to containers, the ability to monitor and track container health and usage is more critical than ever. Many teams are already using the Metricbeat docker module to collect Docker container monitoring data so it can be stored and analyzed in Elasticsearch for further analysis. But what happens when users are using Amazon Elastic Container Service (Amazon ECS)? Can Metricbeat still be used to monitor Amazon ECS? Yes!

We believe that our customers should not have to make compromises in their business process to implement and use AlertOps. AlertOps offers total flexibility, meaning it is highly configurable, legitimately addressing your pain points. One of our core tenets, that we use from our ideation stage of our product roadmap, thinking through the various design aspect to allow the maximum flexibility for the user to configure the software to their needs.

Nobody will dispute that a common goal of DevOps pros and SREs, and really any company today, is to delight their customers more by disappointing them less. This was the theme of a recent live webinar focused on announcing a new game-changing partnership between Datadog and Moogsoft. The live session combined remarks by Moogsoft CEO Phil Tee and CTO Dave Casper on bringing together the best of these two technologies with a new seamless integration.

With increasing complexity and workloads, the world of IT operations is constantly evolving to meet the needs of digital-first organizations. Automation, AI and DevOps are intersecting today like never before. A constant influx of new technologies means new terms. Here's our take on the meaning of leading words and phrases in the space right now.

Security is an essential part of any modern IT foundation, whether in smaller shops or at enterprise-scale. It used to be sufficient to implement rules-based software to defend against malicious actors, but those malicious actors are not standing still. Just as every aspect of IT has become more sophisticated, attackers have continued to innovate as well. Building more and more rules-based software to detect security events means you are always one step behind in an unsustainable fight.

The term network device covers a lot of ground. Anything from a simple unmanaged switch to a dial-up modem to a UTM (unified threat management) appliance can fit the bill. Additionally, the lines between what category different networking devices fall into can get fuzzy because in many cases a single device performs multiple functions. Here, to help you sort through that fuzziness, we tackle the concept of network devices from the ground up.

While the COVID-19 pandemic has disrupted business models around the world, the adoption of modern application and cloud technologies continues to grow. This year’s Continuous Intelligence Report by Sumo Logic provides an inside look into the state of the modern application technology stack, including changing trends in cloud and application adoption and usage by customers, and the impact of COVID-19 as an accelerant for digital transformation efforts.

One of the great things about InfluxDB is that it is really easy to get up and running, and it doesn’t require much monitoring when you are dealing with datasets that fit well on your local dev machine. Once you start using InfluxDB in production and pushing orders of magnitude more data into the system, it’s critical to monitor how your instance is performing so that you can proactively respond to things like disk or network failures, memory saturation, and write or query loads.