Operations | Monitoring | ITSM | DevOps | Cloud

We have been using Azure Functions for years. Being able to easily deploy and run code on both Azure App Services and real serverless has been a killer feature for all of our asynchronous jobs and services. Unfortunately, the logging approach provided as part of the default template is not ideal. In this post, I'll introduce you to the first steps we take in all of our existing and new function apps to improve logging. A quick note about the Azure Functions runtime.

Elastic Observability customers saw 243% ROI and $1.2 million in savings over 3 years For government and education organizations around the world, facilitating an efficient, reliable customer experience is essential when providing critical services and building trust with stakeholders. As technology infrastructure expands and the IT landscape becomes a complex mix of private cloud, public cloud, and air-gapped environments, the ability to see across all systems and data is challenging yet critical.

For the past few months, we’ve been working closely with the LangChain team as they made progress on launching LangServe and LangChain Templates! LangChain Templates is a set of reference architectures to build production-ready generative AI applications. You can read more about the launch here.

Organizations saw a 243% ROI and $1.2 million in savings over three years In today’s complex and distributed IT environments, traditional monitoring falls short. Legacy tools often provide limited visibility across an organization’s tech stack and often at a high cost, resulting in selective monitoring. Many companies are therefore realizing the need for true, affordable end-to-end observability, which eliminates blind spots and improves visibility across their ecosystem.

In this conversation, Sanjay Shrestha, Principal Detection Engineer at Bayer, and Raanan Dagan, Principal Sales Engineer from Cribl, talk about the integration of Git in Cribl Stream. They discuss how to manage configuration files and pipelines as code, simplifying their deployment. They also share a demo and give best practices for optimizing your GitOps workflow. In the 10+ years that Bayer has worked with Splunk, they’ve gone from processing just 80 GB/day to more than 13 TB/day.