Operations | Monitoring | ITSM | DevOps | Cloud

Running your business using Teams isn’t without its challenges. We already did a post here about some of the Microsoft Teams alerts IT teams need to be alerted to sooner rather than later. But, because of how complex large Teams setups are, we’ve got a few more to add to the collection. Today, we’re focusing on the Microsoft-specific challenges you might face.

Cloud native is the de facto standard approach to deploying software applications today. It is optimized for a cloud computing environment, fosters better structuring and management of software deployments. Unfortunately, the cloud native approach also poses additional challenges in code instrumentation that are detrimental to developer productivity.

Developers and teams who want to deploy new code often and safely leverage feature flags to decouple code deployments from feature releases. Feature flags enable teams to release new features to a subset of users, making it possible to test a new feature’s impact on users and ensuring that developers can easily roll back the feature if it causes downstream issues.

Artificial intelligence (AI) and machine learning (ML) are two cutting-edge technologies that are revolutionizing the field of website development. AI refers to the ability of computers to perform tasks that typically require human intelligence, such as recognizing speech, understanding natural language, and making decisions based on data. On the other hand, ML is a subset of AI that involves training algorithms to learn from data and make predictions or decisions based on that learning.

Engineers know best. No machine or tool will ever match the context and capacity that engineers have to make judgment calls about what a system should or shouldn’t do. We built Honeycomb to augment human intuition, not replace it. However, translating that intuition has proven challenging. A common pitfall in many observability tools is mandating use of a query language, which seems to result in a dynamic where only a small percentage of power users in an organization know how to use it.

Time series data streams are often noisy and irregular. But it doesn’t matter if the cause of the irregularity is a network error, jittery sensor, or power outage – advanced analytical tools, machine learning, and artificial intelligence models require their data inputs to include data sets with fixed time intervals. This makes the process of filling in all missing rows and values a necessary part of the data cleaning and basic analysis process.

April has come and gone, and we’ve got more exciting news to show for our efforts! This month, our teams have been hard at work: Keep reading for more details!

Grafana Agent v0.33 is here! This new release includes a lot of exciting features, such as a powerful way to configure Grafana Agent with Flow Modules and the ability to monitor Kubernetes pods in your cluster with an Operator Flow component. We also added many more Flow components making the Flow ecosystem bigger!