Elasticsearch® has been used by developers to build search experiences for over a decade. At Microsoft Build this year, we announced the launch of Elasticsearch Relevance Engine™ — a set of tools to enable developers to build AI-powered search applications. With generative AI, large language models (LLMs), and vector search capabilities gaining mindshare, we are delighted to expand our range of tools and enable our customers in building the next generation of search apps.

In my previous articles, I discussed how to design considerations for observability solutions and how observability can augment your security implementation. In this article, I will discuss how an observability solution can provide valuable insights into your business operations through the collected data from various systems, applications, and services.

AWS Fargate is a serverless pay-as-you-go engine used for Amazon Elastic Container Service (ECS) to run Docker containers without having to manage servers or clusters. The goal of Fargate is to containerize your application and specify the OS, CPU and memory, networking, and IAM policies needed for launch. Additionally, AWS Fargate can be used with Elastic Kubernetes Service (EKS) in a similar manner.

Once upon a time, not so long ago, the world was a different place. The idea of a "smartphone" was still a novelty, and the mobile phone was primarily a tool for making calls and, perhaps, sending the occasional text message. Yes, we had "smart" phones, but they were simpler, mostly geared toward business users and mostly used for, well, phone stuff. Web browsing? It was there, but light, not something you'd do for hours.

In Elasticsearch 8.8, we’re introducing the reroute processor in technical preview that makes it possible to send documents, such as logs, to different data streams, according to flexible routing rules. When using Elastic Observability, this gives you more granular control over your data with regard to retention, permissions, and processing with all the potential benefits of the data stream naming scheme. While optimized for data streams, the reroute processor also works with classic indices.

Elastic customers have seen a 5% revenue improvement within three years, among other benefits. How? Using Elasticsearch, developers can ingest and connect various data sources to provide their companies, employees, customers, and/or public access to information and tune results for faster, more precise answers. Elasticsearch AI/ML powered search is designed to maximize performance and compute resources to deliver applications that can scale as businesses grow.

Generative artificial intelligence (GAI) is undoubtedly one of the biggest trends across industries in 2023. In a recent survey, almost two-thirds of executives believe generative AI will have a high or extremely high impact on their organization in the next three to five years. Executives anticipate spending the next 6–12 months focused on increasing their understanding of how generative AI works, evaluating internal capabilities, and investing in generative AI tools.

Are you prepared for the unexpected? In today's rapidly evolving world, operational resilience has never been more critical for businesses to survive and thrive. Resiliency is the ability of a system to maintain its operations under adverse conditions, including system failures, unexpected surges in user demand, or even security breaches. The heart of many applications, particularly in this era of data-driven decision-making, is the data store or database.

In a previous blog post, we built a small Python application that queries Elasticsearch using a mix of vector search and BM25 to help find the most relevant results in a proprietary data set. The top hit is then passed to OpenAI, which answers the question for us. In this blog, we will instrument a Python application that uses OpenAI and analyze its performance, as well as the cost to run the application.

In a recent blog post, we discussed how ChatGPT and Elasticsearch® can work together to help manage proprietary data more effectively. By utilizing Elasticsearch's search capabilities and ChatGPT's contextual understanding, we demonstrated how the resulting outcomes can be improved. In this post, we discuss how users’ experience can be further enhanced with the addition of facets, filtering, and additional context.