Operations | Monitoring | ITSM | DevOps | Cloud

Grafana Tempo 2.3 has been unleashed upon the world, bringing with it the latest iteration of the vParquet backend! Tempo 2.3 has a little bit of everything, but the headline item here is vParquet3 and new features that improve search speeds. Watch the video above for all the details, or continue reading to get a quick overview of the latest updates in Tempo. If you’re looking for something more in-depth, don’t hesitate to jump into the changelog or our Grafana Tempo 2.3 release notes.

The industrial automation landscape is evolving rapidly, driven by a fundamental shift in how software solutions are developed and deployed. In the past, many factories had in-house IT expertise to build and maintain their basic software stacks, the present reality is quite different. Small-scale factories often lack the IT know-how to manage their increasingly complex digital infrastructure, and they often rely on contractors to set up and maintain their systems.

SAP customers must move to SAP S/4HANA by 2027 to stay compliant, but many are hesitant to do so. Although 78% of organizations in North America are planning an S/4HANA move, only 31% have started using it, according to a CIO article. The UK & Ireland SAP User Group (UKISUG) reports 89% and 25%, respectively. Moving to S/4HANA can take three to four years, according to Roland Berger, and requires a high investment at a time when companies are tightening their belts.

With Kubernetes emerging as the de facto operating system of the cloud, capable of running almost anything, it’s not a surprise that many enterprises are rapidly porting their Apache Spark workloads to Kubernetes. This includes migrating Amazon EMR workloads to Amazon EKS to gain the additional deployment and scaling benefits of a fully managed service like Amazon EKS.

Software production has become exponentially more complex over the last few years as containerization and microservice architectures have exploded. These design decisions are rooted in aspirations of scalability and speed, but left unchecked, have devolved into data model mayhem, development silos, and environment inconsistencies.