YAML sucks! This blog post explains why existing tools hardly ease this pain, and what we at Grafana Labs did about it with our new project, Tanka.

Unless you build or program computers for a living, you could be forgiven for never having wondered what actually happens under the hood. How do computers make decisions? What do their instructions look like? Can they, y’know… think?

When Alaska Airlines deployed AppDynamics, outages were reduced by 60% and mean time to resolution (MTTR) went from hours to less than 10 minutes.

Easying enterprise Kubernetes adoption is at the core of what we do. Today, we are thrilled to announce Kublr’s simplified and comprehensive role-based-access-control (RBAC) management via the Kublr UI.

In the third of our series of articles, Yan Cui highlights the key insights from the Amazon Builders’ Library article, Avoiding fallback in distributed systems, by AWS Senior Principal Engineer Jacob Gabrielson.

In the early days of Amazon, Jeff Bezos set a rule: teams shouldn’t be larger than what two pizzas can feed, no matter how large a company gets. Setting this rule of small teams meant individuals spent less time providing status updates to each other and more time actually getting stuff done. It also allowed team members more time to focus on continuous improvement. PagerDuty, like Amazon, has a strong culture of continuous improvement.

As part of Elasticsearch 7.5.0, we introduced a couple of ways to control the index age math that’s used by index lifecycle management (ILM) for phase timings calculations using the origination_date index lifecycle settings. This means you can now tell Elasticsearch how old your data is, which is pretty handy if you’re indexing data that’s older than today-days-old.

Traditional enterprise application platforms are usually built with Java Enterprise technologies and this is the case as well for OpsRamp. However, in machine learning (ML) world, Python is the most commonly used language, with Java rarely used. To develop ML components within enterprise platforms, such as the AIOps capabilities in OpsRamp, we have to run ML components as Python microservices and they communicate with Java microservices in the platform.

A few blogs posts ago I wrote about new BI for digital companies and in that blog I alluded that quite a bit of that BI is based on log data. I wanted to follow up on the topic of logs, why they exist and why they contain so much data that is relevant to BI. As I said in that post, logs are an artifact of software development and they are not premeditated, they are generated by developers almost exclusively for the purpose of debugging pre-production code. So how is it that logs are so valuable for BI?

It is probably the best time to be a designer right now since there are many online tools and software that targets designing pain points and simplifies them. From the creation of an idea down to its realization; designers can find appropriate tools that will help them in drawing out their thoughts, figuring out the complexities, and finding out how the users interact with their designs.