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.

Last month, we ran a webinar on role-based access control (RBAC) in Sensu Go. In this post, we’ll capture some of the key points from the presentation and show you how RBAC can help you lock down access for increased security and team efficiency.

Istio is an open source service mesh that was released in 2017 as a joint project from Google, IBM, and Lyft. By abstracting the network routes between services from your application logic, Istio allows you to manage your network architecture without altering your application code. Istio makes it easier to implement canary deployments, circuit breakers, load balancing, and other architectural changes, while also offering service discovery, built-in telemetry, and transport layer security.

In Part 1, we showed you the metrics that can give you visibility into your Istio service mesh and Istio’s internal components. Observability is baked into Istio’s design—Mixer extracts attributes from traffic through the mesh, and uses these to collect the mesh-based metrics we introduced in Part 1. On top of that, each Istio component exposes metrics for its own internal workings.

In Part 2, we showed you how to use Istio’s built-in features and integrations with third-party tools to visualize your service mesh, including the metrics that we introduced in Part 1. While Istio’s containerized architecture makes it straightforward to plug in different kinds of visualization software like Kiali and Grafana, you can get deeper visibility into your service mesh and reduce the time you spend troubleshooting by monitoring Istio with a single platform.