In Elasticsearch, an index is a logical container or namespace that holds a collection of documents that are related in some way. It is the primary unit for organizing and storing data. Indices in Elasticsearch serve as containers for organizing and managing data, enabling efficient search and retrieval operations. Understanding the concept of indices is crucial for effective data organization and utilization in Elasticsearch-based applications.

In this guide, we will compare two of the leading data visualization tools based upon open-source software that are available for use for metrics, traces and log analysis. To allow new users to know exactly which solution may be best suited to their needs, we wanted to explore in more depth a comparison between OpenSearch Dashboards and Kibana across various aspects in our latest guide covering the differences between leading open-source software.

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.

In this tutorial, we will guide you through the process of migrating from Elasticsearch to OpenSearch. OpenSearch is aan open-source search and analytics suite that is compatible with Elasticsearch. There are several reasons why people choose to migrate, such as taking advantage of new features or differences in governance. In the following sections, we will discuss version compatibility considerations, and guide you through the migration process.

In the ever-evolving world of search engines, Elasticsearch, OpenSearch, and Solr have long held the spotlight. However, there are several smaller search platforms that pack a punch and offer compelling alternatives. In this article, we will explore 11 small search platforms, delving into their major features, pros, and cons.

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.

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.

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.