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.

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.

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.

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.

Terry Mulligan, is a Splunk consultant with Discovered Intelligence (and Notre Dame’s biggest fan)— a data intelligence services and solutions provider that specializes in data observability and security platforms. He shares what Cribl has brought to the table for his organization and his clients, and how it’s changed their processes and the role of the Splunk data engineer.

Simply put, value stream management (VSM) is the practice of measuring and improving the flow of business value created by an organization’s software delivery efforts.By monitoring the software delivery life cycle end-to-end, organizations can better identify processes that add value and eliminate those that create waste to optimize the flow of work. Ultimately, this enables teams to move away from activities that don’t directly contribute to customer value and focus more on those that do.

We continue our exploration of the fascinating world of Kubernetes, logs, and metrics. In our previous installment, we delved into the intricate tale of Cribl Edge and its role in unraveling the mysteries of logging and metrics in Kubernetes environments with the Cribl Edge native sources for Kubernetes Metrics and Logs. Today, we’re picking up where we left off, shining a spotlight on a new and powerful tool that has the potential to demystify this complex ecosystem further.

Simply put, customer analytics (or customer data analytics) is the process of using information about customer preferences and behavior to improve sales, marketing and product development. You can think of customer analytics as the type of customer behavior where buyers are doing internet research before making a purchase. There is now a vast amount of information available for nearly every product category online.

Businesses today rely on analytics and insights derived from different data types for gaining competitive advantages. These data often come from different sources and in different formats. Without a unified solution, aggregating those data and performing analytics tasks is challenging. ELT has been invented to solve the complexities associated with processing data from multiple sources while retaining the raw data as it is.

A data platform is a comprehensive end-to-end solution for all your data. A true data platform can ingest, process, analyze and present data generated by all the systems and infrastructures within your organization. In this topic, there’s a lot of things to understand and consider. So, let’s take a deep look at data platforms, including the definition and related terms, the benefits and use cases, and how to start building your data strategy.