Prashant Agrawal
Sr. OpenSearch Spec Solutions Architect
Seattle, Washington, United States
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Prashant is a Sr. OpenSearch Specialist Solutions Architect. He works closely with customers to help them migrate their workloads to the cloud and helps existing customers fine-tune their clusters to achieve better performance and save on cost. Before joining AWS, he helped various customers use OpenSearch and Elasticsearch for their search and log analytics use cases. When not working, you can find him traveling and exploring new places. In short, he likes doing Eat → Travel → Repeat.
Area of Expertise
Topics
From Keywords to Intent—Why It’s Time to Rethink Search
In the age of generative AI, search expectations have changed. Users now want more than just keyword matches - they expect intelligent, context-aware results. Whether you're building a recipe recommendation engine or enhancing e-commerce discovery, vector search offers a powerful upgrade to the overall search ecosystem.
This shift is driven by the rise of generative AI models that understand natural language. Users now expect search to behave more like a conversation and less like a command-line filter. Traditional keyword-based search is hitting its limits, as it relies on exact or fuzzy matches of words in metadata, missing opportunities to truly understand user intent.
In this talk, we'll explore a practical example of how you can enhance your keywords search with vector search, powered by embeddings and semantic understanding, can dramatically improve the search experience. For a query like "Quick, healthy dinner with tofu, no dairy", a vector search engine can go beyond simple keyword matching to:
Identify recipes labeled as "30-minute meals" as relevant for "quick"
Surface dishes that are nutrient-dense, even without an explicit "healthy"
Achieve up to 50% Reduction in Storage Costs by Optimizing Mapping for Your Logging Workload
In this session, we will explore the critical strategies for optimizing mapping configurations to achieve a significant reduction in storage costs for your logging workload. As organizations generate vast amounts of log data, managing storage efficiently becomes a top priority. We will delve into the best practices for configuring index mappings to minimize storage requirements without compromising the integrity and accessibility of log data. By implementing these optimizations, participants will learn how to reduce their storage costs by up to 50%. We will cover techniques such as filtering out noisy logs, utilizing data optimization hubs to identify and trim excess log volume, and applying advanced compression settings. Through practical examples and real-world case studies, attendees will gain actionable insights into configuring their logging workloads for maximum storage efficiency and cost savings.
Mastering OpenSearch Migration: From Assessment to Production with Zero Downtime
Organizations migrating from Elasticsearch to OpenSearch face the challenge of ensuring business continuity during the transition. This hands-on session demonstrates practical migration approaches using OpenSearch Migration Assistant, focusing on two powerful strategies: reindex from snapshot and live capture-replay.
Through live demonstrations, we'll showcase the Migration Assistant's capabilities in pre-migration assessment, identifying compatibility issues, and executing the migration. We'll illustrate how "reindex from snapshot" efficiently handles historical data while maintaining index settings and mappings. The session will then explore the live capture-replay mechanism for managing real-time data updates during migration with minimal latency.
Whether you're managing terabytes of historical data or handling high-throughput real-time indexing, this session provides practical strategies and hands-on experience with Migration Assistant features. Join us to bridge the gap between theory and practice, gaining actionable insights for a successful OpenSearch migration.
Boosting AI with OpenSearch: Vector Stores, Similarity Search, and Retrieval-Augmented Generation
Explore how OpenSearch enhances AI applications through vector stores, similarity search, and retrieval-augmented generation (RAG). Learn to efficiently store and retrieve high-dimensional data, improve recommendation systems with similarity search, and integrate robust indexing for context-aware responses using RAG. This session provides practical insights and examples for developers, data scientists, and AI enthusiasts to optimize their AI solutions with OpenSearch. Join us to elevate your AI capabilities with advanced search technologies.
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