Category: Amazon OpenSearch Service

In this post, we walk you through how to configure service provider-initiated authentication for OpenSearch Dashboards by using OpenSearch Service and Keycloak. We also discuss how to set up users, groups, and roles in Keycloak and configure their access to OpenSearch Dashboards.

We’re thrilled to announce the launch of the official Amazon OpenSearch Service YouTube channel—a comprehensive resource for anyone looking to master Amazon OpenSearch Service. Whether you’re just getting started with searches , vectors, analytics, or you’re looking to optimize large-scale implementations, our channel can be your go-to resource to help you unlock the full potential of OpenSearch Service.

This post provides a detailed walkthrough about how to efficiently capture and manage manual snapshots in OpenSearch Service. It covers the essential steps for taking snapshots of your data, implementing safe transfer across different AWS Regions and accounts, and restoring them in a new domain. This guide is designed to help you maintain data integrity and continuity while navigating complex multi-Region and multi-account environments in OpenSearch Service.

We recently announced a new capacity level of 30TB for time series data per account per AWS Region. The OpenSearch Serverless compute capacity for data ingestion and search/query is measured in OpenSearch Compute Units (OCUs), which are shared among various collections with the same AWS Key Management Service (AWS KMS) key. This post discusses how you can analyze 30TB time series datasets with OpenSearch Serverless.

In this post, we outline two solutions that provide cross-Region resiliency without needing to reestablish relationships during a failback, using an active-active replication model with Amazon OpenSearch Ingestion (OSI) and Amazon Simple Storage Service (Amazon S3). These solutions apply to both OpenSearch Service managed clusters and OpenSearch Serverless collections. We use OpenSearch Serverless as an example for the configurations in this post.

Amazon OpenSearch Service Pricing is based on three dimensions: instances, storage, and data transfer. Storage pricing depends on the chosen storage type and also the storage tier. Visibility into domain-level charges enables accurate budgeting, efficient resource allocation, fair cost attribution across projects, and overall cost transparency. In this post, we show you how to view the OpenSearch Service domain-level cost using AWS Cost Explorer.

Today the Linux Foundation announced the OpenSearch Software Foundation. As part of the creation of the OpenSearch Foundation, AWS has transferred ownership of OpenSearch to the Linux Foundation. At the launch of the project in April of 2021, in introducing OpenSearch, we spoke of our desire to “ensure users continue to have a secure, high-quality, fully open source search and analytics suite with a rich roadmap of new and innovative functionality.” We’ve maintained that desire and commitment, and with this transfer, are deepening that commitment, and bringing in the broader community with open governance to help with that goal.

While the potential of generative artificial intelligence (AI) is increasingly under evaluation, organizations are at different stages in defining their generative AI vision. In many organizations, the focus is on large language models (LLMs), and foundation models (FMs) more broadly. This is just the tip of the iceberg, because what enables you to obtain differential […]

In this blog post, we will highlight how ZS Associates used multiple AWS services to build a highly scalable, highly performant, clinical document search platform. This platform is an advanced information retrieval system engineered to assist healthcare professionals and researchers in navigating vast repositories of medical documents, medical literature, research articles, clinical guidelines, protocol documents, […]

In this post, instead of using the BM25 algorithm, we introduce sparse vector retrieval. This approach offers improved term expansion while maintaining interpretability. We walk through the steps of integrating sparse and dense vectors for knowledge retrieval using Amazon OpenSearch Service and run some experiments on some public datasets to show its advantages.