Open source software refers to computer programs with source code available for anyone to inspect, modify, and distribute. Unlike proprietary software, open source software is developed collaboratively by a community of developers. One of the main benefits of open source software is cost savings. Because the source code is freely available, organizations can use and customize the software without paying licensing fees, reducing costs, especially for large-scale deployments.

Our recent virtual event with Cohere dove deep into the world of retrieval augmented generation (RAG), focusing on the critical considerations for building RAG applications beyond the proof-of-concept stage. Our speakers, Lily Adler, principal solutions architect at Elastic, and Maxime Voisin, senior product manager at Cohere, shared valuable insights on the challenges, solutions, and best practices in this evolving field of natural language processing (NLP).

Today, open source software is everywhere – from Linux-based servers, to Android smartphones, to the Firefox Web browser, to name just a handful of open source platforms in widespread use today. But the open source code driving these innovations doesn't write itself. It's developed by open source contributors – and you could be one of them.

This is the first of a planned series of blog posts that explain how the Cribl SRE team builds, optimizes, and operates a robust Observability suite using Cribl’s products, Cribl.Cloud operates on a single-tenant architecture, providing each customer with dedicated AWS accounts furnished with ready-to-use Cribl products. This provides our customers with strict data and workload isolation but presents some interesting and unique challenges for our Infrastructure and operations.

As modern IT infrastructure becomes increasingly complex, businesses generate massive amounts of logs compared to the past in real time. Therefore, streamlining this unstructured log data into a more structured form becomes vital with this growing complexity. Organizations must collect unstructured log data from various sources, extract meaning from them, and store them in a centralized repository. That’s where Log Aggregation comes in.

Server monitoring involves continuously observing and tracking the performance, availability, and health of servers within an IT infrastructure and is a vital process for organizations aiming to enhance their servers. By conducting server monitoring, with the assistance of server monitoring tools, your organization can detect issues such as hardware failures or software glitches promptly allowing for quick resolutions as server monitoring tools continuously track server health and performance metrics.

Like many other organizations, we at Mezmo struggle with a lot of telemetry data, and for a while our team configured our logs to be sent to a global Mezmo Log Analysis account in our SaaS so we would have a single pane of glass to view all of our logs. Our SRE team wanted to make sure that we have experience utilizing our new pipeline product. We set out some goals before we started using telemetry pipeline.

Today, we are pleased to announce the general availability of Elastic 8.15.

Elastic Observability 8.15 announces several key capabilities: New and enhanced native OpenTelemetry capabilities: Elastic AI Assistant enhancements: Large language model (LLM) observability for Azure OpenAI: Elastic Observability now provides deep visibility on the usage of the Azure OpenAI Service. The integration includes an out-of-the-box dashboard that summarizes the most relevant aspects of the service usage, including request and error rates, token usage, and chat completion latency.

In 8.15, great search results are even more accessible for our customers. Our latest release brings semantic reranking, additional vector search tools, and more third-party model providers and promotes our native Learning to Rank (LTR) to generally available. And now search is more performant than ever with additional speed and efficiency improvements.