As the automation surface area grows to accommodate hundreds of interconnected APIs on the cloud, developers are using their own, home-grown “digital duct tape” to manage a growing “DevOps dumping ground”. For a lot of organizations, home-grown glue logic is inconsistent, not repeatable, and expensive to maintain hundreds of event-based workflows and thousands of combinations. We believe that the answer lies in automation workflows.

There’s no debating the value of digital transformation when it comes to optimal business performance, but for many IT leaders, some digital initiatives are pushed to the backburner due to other priorities. Over the last few months, COVID-19 has forced a number of those initiatives to the top of the list. From businesses transitioning to remote work, to schools closing, to travel plans being canceled, no one was fully prepared for what the world would face this year.

Jessica Deen’s swampUP 2020 talk takes Kubernetes to the next level. If you want to understand how to make your K8s implementation enterprise grade, then this is the session for you. JFrog was proud to announce Jessica Deen, Microsoft Azure Avenger, as the winner of the inaugural swampUP Carl Quinn Speaker Award. Jessica’s talk was full of demos, practical advice, and covered a wide range of real-world situations.

Our latest announcement comes in the form of another exciting partnership, this time with Qualys! A leading provider of disruptive cloud-based IT, security, and compliance solutions, Qualys has integrated Ivanti Patch Management technology into their Qualys VMDR (Vulnerability Management Detection Response) platform.

Year after year, we have seen that when it comes to maintaining end-user experience during the holiday season, preparation is key. But this year promises to be different, there has never been a time where digital-first was so imperative to the survival of most retailers. The drastic changes brought in by COVID-19 is bound to have a significant impact on the holiday season. Black Friday 2020 might as well be the benchmark for DEM going forward.

We’re excited to announce a new set of product updates and enhancements to the PagerDuty platform! PagerDuty partners with organizations to help teams create efficiencies across IT organizations and protect customer relationships. These updates will help further improve your team’s ability to manage and reduce noise, automate critical response workflows, and quickly mobilize a response in order to mitigate disruptions across your digital operations when seconds matter.

Have you heard the buzz about observability with Honeycomb 🐝? It’s the best tool on the market for observing your systems in real time to reduce toil and delight users. But don’t listen to us, listen to our buzzing community of “Pollinators”–this blog post is dedicated to them 💖 An invite is sent to our shared Slack workspace “Pollinators” to everyone who signs up for Honeycomb.

As well as being a search engine, Elasticsearch is also a powerful analytics engine. However, in order to take full advantage of the near real-time analytics capabilities of Elasticsearch, it is often useful to add structure to your data as it is ingested into Elasticsearch. The reasons for this are explained very well in our schema on write vs. schema on read blog post, and for the remainder of this blog series, when I talk about structuring data, I am referring to schema on write.

There’s no getting around the prevalence of heterogeneous tools in IT operations and DevOps. Teams have built up ecosystems of tools over time for different infrastructure, workflows and applications – and while some of them are outdated or duplicitous – plenty still serve a useful purpose in the enterprise. Yet unfortunately tool sprawl adds to the complexity of monitoring a hybrid or multi-cloud environment.

I am spending a considerable amount of time recently on distributed tracing topics. In my previous blog, I discussed different pros and cons of various approaches to collecting distributed tracing data. Right now I would like to draw your attention to the analysis back-end: what does it take to be good at analyzing transaction traces?