Tag: Amazon OpenSearch

This post demonstrates how to build a secure search application using Amazon OpenSearch Service and JSON Web Tokens (JWTs). We discuss the basics of OpenSearch Service and JWTs and how to implement user authentication and authorization through an existing identity provider (IdP). The focus is on enforcing fine-grained access control based on user roles and permissions.

In this post, we share field-tested patterns for log ingestion that have helped organizations successfully implement logging at scale, while maintaining optimal performance and managing costs effectively. A well-designed log analytics solution can help support proactive management in a variety of use cases, including debugging production issues, monitoring application performance, or meeting compliance requirements.

This post demonstrates how to harness OpenSearch Dashboards to analyze logs visually and interactively. With this solution, IT administrators, developers, and DevOps engineers can create custom dashboards to monitor system behavior, detect anomalies early, and troubleshoot issues faster through interactive charts and graphs.

In this post, we walk you through a search application building process using Amazon OpenSearch Service. Whether you’re a developer new to search or looking to understand OpenSearch fundamentals, this hands-on post shows you how to build a search application from scratch—starting with the initial setup; diving into core components such as indexing, querying, result presentation; and culminating in the execution of your first search query.

Amazon OpenSearch Service also offers a multi-tiered storage solution to its customers in the form of UltraWarm and Cold tiers. In this post, we discuss this new capability and its use cases, and provide a cost-benefit analysis in different scenarios.

This post showcases how to use Spark on AWS Glue to seamlessly ingest data into OpenSearch Service. We cover batch ingestion methods, share practical examples, and discuss best practices to help you build optimized and scalable data pipelines on AWS.

This blog post introduces a new disk-based vector search approach that allows efficient querying of vectors stored on disk without loading them entirely into memory. By implementing these quantization methods, organizations can achieve compression ratios of up to 64x, enabling cost-effective scaling of vector databases for large-scale AI and machine learning applications.

In this post, we show you how to manage user access to enterprise documents in generative AI-powered tools according to the access you assign to each persona. This post illustrates how to build a document search RAG solution that makes sure only authorized users can access and interact with specific documents based on their roles, departments, and other relevant attributes. It combines OpenSearch Service and Amazon Cognito custom attributes to make a tag-based access control mechanism that makes it straightforward to manage at scale.

OpenSearch Service stores different types of stored objects, such as dashboards, visualizations, alerts, security roles, index templates, and more, within the domain. As your user base and number of Amazon OpenSearch Service domains grow, tracking activities and changes to those saved objects becomes increasingly difficult. In this post, we present a solution to deploy stored objects using GitHub and Jenkins while preventing users making direct changes into OpenSearch Service domain

In this post, we’ll explore Infor’s journey to modernize its search capabilities, the key benefits they achieved, and the technologies that powered this transformation. We’ll also discuss how Infor’s customers are now able to more effectively search through business messages, documents, and other critical data within the ION OneView platform.