Operations | Monitoring | ITSM | DevOps | Cloud

We are excited to introduce a new capability in Bitbucket Pipelines – Step Failure Strategies. This is the first of a set of new features allowing developers to implement more comprehensive logic and control-flow inside their CI/CD pipelines. Failure Strategies are designed to give you explicit control over how your pipeline behaves in the event that an individual step within the pipeline fails.

We are excited to announce the release of ARM builds in the Pipelines cloud runtime. Our release of Linux based ARM runners in cloud allows you to build and deploy software for ARM-based systems with all the benefits of our fully managed CI/CD platform. To use the new cloud ARM runners in your pipeline, make the following modifications to your bitbucket-pipelines.yml file.

The Kosli CLI provides several attest commands, such as kosli attest snyk, kosli attest jira, etc. These attestations are “typed” - each one knows how to interpret its own particular kind of input. For example, kosli attest snyk interprets the sarif file produced by a snyk container scan to determine the true/false value for that individual attestation.

Many companies migrate to the cloud and overlook costs in favor of innovation, speed, and flexibility. They assume that the cloud is inherently more cost-effective than on-premises infrastructure. However, the organizations soon realize that the same characteristics that make the cloud such an enticing and flexible resource can also lead to unexpectedly higher usage bills than expected. The challenge is to find the right balance between optimal system performance, engineering velocity, and cost.

In our last blog post we talked about how you can use Intel QAT with Canonical Ceph, today we’ll cover why this technology is important from a business perspective – in other words, we’re talking data storage costs. Retaining and protecting data has an inherent cost based on the underlying architecture of the system used to store it.

So, you’ve got a PostgreSQL database packed with juicy data, and you want to turn those raw numbers into slick, interactive Grafana dashboards? Good call! Grafana’s PostgreSQL datasource is like the secret handshake that lets you visualize your data in style—no extra ETL magic required. In this guide, we’ll walk through getting PostgreSQL and Grafana to play nice, covering everything from connection settings to query tuning.

Logs are crucial for understanding what's happening in your system, but they can often be hard to make sense of. Log parsing is the key to turning raw, unstructured data into something useful. In this blog, we'll explore the basics of log parsing, its importance, and how it helps you extract valuable insights from your logs without all the clutter.

Most developers are familiar with Opentelemetry core components—Traces, Metrics, and Logs. But there’s one part of the OpenTelemetry ecosystem that doesn’t always get the spotlight: processors. These behind-the-scenes operators shape your data pipeline, helping you filter, enrich, and fine-tune telemetry data before it reaches your backend systems. Processors play a key role in making sure your data is cleaner, more useful, and just the way you need it.