Operations | Monitoring | ITSM | DevOps | Cloud

When Grafana Labs CEO and co-founder Raj Dutt announced to the team that the company would be relicensing our core open source projects from Apache 2.0 to AGPLv3, he opened the floor for discussion and encouraged anyone who had further questions to reach out. We believe in honesty and transparency, so we collected hard questions from Grafanistas, and Raj answered them for this public Q&A. The time felt right. As I’ve said publicly before, I’ve been thinking about this topic for years.

A common challenge in data engineering is to combine traditional data warehousing and BI reporting with experiment-driven machine learning projects. Many data scientists tend to work more with Python and ML frameworks rather than SQL. Therefore, their data needs are often different from those of data analysts. In this article, we’ll explore why having a data lake often provides tremendous help for data science use cases.

Telecom companies monitor their network using a variety of monitoring tools. There are separate fault management and performance management platforms for different areas of the network (core, RAN, etc.), and infrastructure is monitored separately. Although these solutions monitor network functions and logic – something that would seem to make sense — in practice this strategy fails to produce accurate and effective monitoring or reduce time to detection of service experience issues.

Back in December, Amazon Web Services (AWS) and Grafana Labs partnered to launch the Amazon Managed Service for Grafana in a preview to a limited set of customers. Amazon Managed Service for Grafana is a scalable managed offering that provides AWS customers a native way to run Grafana directly within AWS alongside all their other AWS services.

Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. This week we explore the rising scope of location intelligence in improving the overall customer experience.

Starting today, Honeycomb’s Management API is generally available to all Honeycomb users. The Honeycomb Management API is a set of endpoints that lets you programmatically set up, configure, and delete queries, datasets, derived columns, and more. With this release, you can now manage Honeycomb with configuration as code either directly via API or with third-party tools, like Terraform, using the community-contributed Honeycomb provider.

APIs are the backbone of software products. Whether the APIs are customer facing or for internal use, making sure that your APIs are up and running is crucial. In this post, we will see how to get started with API monitoring with Checkly.

Why we felt there was a need for a full stack open source observability platform and how we went about building it.

In the last blog post, I walked you through how to connect to the Microsoft Graph API so you can start pulling in the M365 analytics to create a dashboard in SquaredUp. In this blog post, I’ll walk you through exactly how to create this dashboard. This dashboard will allow you to monitor key metrics for Microsoft 365 SharePoint, Exchange Online, and Teams so you can be proactive in assigning storage.

Large amounts of data no longer reside within siloed applications. A global workforce, combined with the growing need for data, is driving an increasingly distributed and complex attack surface that needs to be protected. Sophisticated cyberattacks can easily hide inside this data-centric world, making traditional perimeter-only security models obsolete.