Operations | Monitoring | ITSM | DevOps | Cloud

Today, at Microsoft Ignite, the release of Azure Monitor SCOM Managed Instance (SCOM MI) was announced. In recent years, the IT industry has increasingly gravitated towards cloud-based solutions. Microsoft, as a leader of innovation, has been at the forefront of this shift, by consistently developing the best possible cloud alternatives for their products.

Sentry started life in 2008 as an unlicensed, 71-line Django plugin. The next year we began publishing it under BSD-3, and ten years later we switched to the Business Source License (BSL or BUSL). Last year we purchased Codecov, and a few months ago we published it under BSL/BUSL as well. That led to some vigorous debate because of our use of the term “Open Source” to describe Codecov, from which emerged this helpful suggestion from Adam Jacob, co-founder of Chef.

Technical teams use various metrics and indicators to track performance and success. For DevOps teams, among the most important metrics is deployment frequency. Deployment frequency can help you evaluate the software delivery performance of teams that develop software and apps. In this article, I’ll look at using this metric to calculate deployment rate, the importance and best practices for improving your deployment rate and setting your DevOps team up for success.

The Splunk Add-on for OpenTelemetry Collector is a variation of the Splunk Distribution of the OpenTelemetry Collector that simplifies metrics and traces data collection, configuration and management. Since it is an add-on, users can deploy it alongside Universal Forwarders using tools like Deployment Server to start collecting high-fidelity metrics and traces from 1000s of their hosts easily. We’re happy to announce that the Add-On is now generally available in Splunkbase.

Application tracing may be getting all the hype these days and rightfully so, as monolithic Java and.Net applications give way to microservices-based applications in modern IT environments. Distributed tracing provides visibility into the flow of requests between the microservices that make up these applications, helping you to spot performance and network connectivity issues.

In the era of big data, choosing the right data storage solution is crucial for organizations to harness the power of their data. Understanding the differences and benefits of data lakes and data warehouses can help businesses make informed decisions on which option best suits their needs. In this blog post, we will explore data lakes and data warehouses, their architecture, and their key features, enabling you to make the right choice for your organization.

Software systems are increasingly complex. Applications can no longer simply be understood by examining their source code or relying on traditional monitoring methods. The interplay of distributed architectures, microservices, cloud-native environments, and massive data flows requires an increasingly critical approach: observability.

Relabeling is an important feature that allows users to modify metadata (labels) of scraped metrics before they ever make it to the database. As an example, some of your scrape targets may generate metric labels with underscores (_), and some of your targets may generate labels with hyphens (-). Relabeling allows you to make this consistent, making database queries easier to write.

In Part 1 of this blog series, I proposed that closed-loop orchestration (CLO) is a data management problem. Data extends beyond your medical history, social media, and other PII in the digital world. Configurations, inventories, and monitoring systems are based on managing, interpreting, and actioning data. CLO is a workflow about manipulating the data around the target environments. Even the policies and constraints need to be expressed as data.