Learn how to harness the power of OpenSearch effectively with 'The Definitive Guide to OpenSearch'. This book explores installation, configuration, query building, and visualization, guiding readers through practical use cases and real-world implementations. Whether you're building search experiences or analyzing data patterns, this guide equips you thoroughly. What this Book will help me do Understand core OpenSearch principles, architecture, and the mechanics of its search and analytics capabilities. Learn how to perform data ingestion, execute advanced queries, and produce insightful visualizations on OpenSearch Dashboards. Implement scaling strategies and optimum configurations for high-performance OpenSearch clusters. Explore real-world case studies that demonstrate OpenSearch applications in diverse industries. Gain hands-on experience through practical exercises and tutorials for mastering OpenSearch functionality. Author(s) Jon Handler, Soujanya Konka, and Prashant Agrawal, celebrated experts in search technologies and big data analysis, bring their years of experience at AWS and other domains to this book. Their collective expertise ensures that readers receive both core theoretical knowledge and practical applications to implement directly. Who is it for? This book is aimed at developers, data professionals, engineers, and systems operators who work with search systems or analytics platforms. It is especially suitable for individuals in roles handling large-scale data, who want to improve their skills or deploy OpenSearch in production environments. Early learners and seasoned experts alike will find valuable insights.

When you look at operational analytics and business data analysis activities—such as log analytics, real-time application monitoring, website search, observability, and more—effective search functionality is key to identifying issues, improving customers experience, and increasing operational effectiveness. How can you support your business needs by leveraging ML-driven advancements in search relevance? In this report, authors Jon Handler, Milind Shyani, Karen Kilroy help executives and data scientists explore how ML can enable ecommerce firms to generate more pertinent search results to drive better sales. You'll learn how personalized search helps you quickly find relevant data within applications, websites, and data lake catalogs. You'll also discover how to locate the content available in CRM systems and document stores. This report helps you: Address the challenges of traditional document search, including data preparation and ingestion Leverage ML techniques to improve search outcomes and the relevance of documents you retrieve Discover what makes a good search solution that's reliable, scalable, and can drive your business forward Learn how to choose a search solution to improve your decision-making process With advancements in ML-driven search, businesses can realize even more benefits and improvements in their data and document search capabilities to better support their own business needs and the needs of their customers. About the authors: Jon Handler is a senior principal solutions architect at Amazon Web Services. Milind Shyani is an applied scientist at Amazon Web Services working on large language models, information retrieval and machine learning algorithms. Karen Kilroy, CEO of Kilroy Blockchain, is a lifelong technologist, full stack software engineer, speaker, and author living in Northwest Arkansas.