Are you interested to learn about the characteristics of Elasticsearch for vector search and what the design looks like? As always, design decisions come with pros and cons. This blog aims to break down how we chose to build vector search in Elasticsearch.
Maybe you came across the term “vector database” and are wondering whether it’s the new kid on the block of data retrieval systems. Maybe you are confused by conflicting claims about vector databases. The truth is, the approach used by vector databases has been around for a few years.
In today's fast-paced digital landscape, the ability to monitor and optimize application performance is crucial for organizations striving to deliver exceptional user experiences. At Elastic, we recognize the significance of providing our user base with a reliable Observability platform that scales with you as you’re onboarding thousands of services that produce terabytes of data each day.
The retail and consumer packaged goods (CPG) industry has undergone significant transformations due to advancements in technology. Technological innovations have reshaped various aspects of the industry, including customer engagement, inventory optimization, and supply chain management. These innovations have helped drive digital transformation, improve operational efficiency, enhance the customer experience, and promote sustainability.
In this post I will introduce sysgrok, a research prototype in which we are investigating how large language models (LLMs), like OpenAI's GPT models, can be applied to problems in the domains of performance optimization, root cause analysis, and systems engineering. You can find it on GitHub.
Arguably, OpenTelemetry exists to (greatly) increase usage of tracing and metrics among developers. That said, logging will continue to play a critical role in providing flexible, application-specific, event-driven data. Further, OpenTelemetry has the potential to bring added value to existing application logging flows.
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.
