Operations | Monitoring | ITSM | DevOps | Cloud

New Relic is known for empowering the world’s leading engineering teams to deliver great software performance and reliability. And the network that delivers that service to New Relic’s users plays a critical role. Hiccups in the performance of the network between New Relic’s mission-critical service and their users can create a cascade of problems.

We are excited to share the latest advances around the Deep Learning Toolkit App for Splunk (DLTK). Earlier this year, Splunk’s Machine Learning Toolkit (MLTK) was updated with some important changes. Please refer to the blog post Driving Data Innovation with MLTK v5.3 and the official documentation to learn more about what changes were made and most importantly how they may affect you, especially if you run MLTK models in production.

In today’s digital world, application responsiveness isn’t just an end-user desire but an expectation. Nobody understands the challenges of meeting these demands better than IT, which must remain poised to act when an issue occurs while also finding ways to innovate, improve, and implement continuously.

Over the past year, Grafana Labs has grown from 300 to 700 Grafanistas. Moving forward, we expect to continue to maintain a high rate of change, and to sustain that, we need to ensure there is flexibility in how our teams* are set up. The majority of our Engineering squads have changed in size and structure — and the same goes for the Grafana Observability team, where I work.

We’re thrilled to announce several new observability features for the Pub/Sub to Splunk Dataflow template to help operators keep a tab on their streaming pipeline performance. Splunk Enterprise and Splunk Cloud customers use the Splunk Dataflow template to reliably export Google Cloud logs for in-depth analytics for security, IT or business use cases.

Adoption of AWS Lambda functions in cloud-native applications has increased exponentially over the past few years. Serverless functions, such as the AWS Lambda service, provide a high level of abstraction from the underlying infrastructure and orchestration, given these tasks are managed by the cloud provider. Software development teams can then focus on the implementation of business and application logic.

Dear Miss O11y, How is modern observability using tracing, such as Honeycomb, different from the previous distributed tracing software I'm familiar with, like Dapper, at my company? I haven't really been able to wrap my head around Dapper. Does "advanced" observability mean that it's even more complicated than Dapper is? Auntie Alphabet.