3-4 June, 2025 | Bengaluru, India View More Details & Registration IMPORTANT NOTE: Timing of sessions and room locations are subject to change.
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As enterprises grapple with unstructured data, Retrieval-Augmented Generation (RAG) is transforming search from simple retrieval to context-aware AI-driven knowledge generation.
This session explores how DeepSeek-R1, a powerful reasoning model, integrates with Amazon OpenSearch Service’s vector database to enable scalable, intelligent information retrieval.
Attendees will learn: How RAG enhances traditional search using vector embeddings and semantic understanding. How to connect DeepSeek-R1 on SageMaker with OpenSearch for AI-powered responses. How OpenSearch’s flexible connectors integrate with models like DeepSeek, Cohere, and OpenAI.
By the end of this session, you'll gain actionable insights on implementing RAG workflows in OpenSearch, improving search accuracy, and advancing the AI ecosystem with knowledge-augmented AI.
Associate Delivery Consultant - Data Analytics, Amazon Web Services (AWS), Amazon Web Services
Shubham is an Associate Delivery Consultant at Amazon Web Services, based in Bangalore. He specializes in Big Data, Data Lakes, ETL Migrations, Search and Observability, as well as GenAI projects. With deep technical expertise, He consistently delivers impactful and scalable solutions... Read More →
Ramya is a AWS Delivery Consultant specializing in Search, Data Warehouse, and ETL solutions. With experience in data engineering and architecture, she has shared her expertise at public technology conferences including PostgreSQL Conference (PGConf), APJC Tech Summit and Amazon wide... Read More →
Wednesday June 4, 2025 11:15am - 11:55am IST Jupiter
Aditya Soni is a DevOps/SRE tech professional He worked with Product and Service based companies including Red Hat, Searce, and is currently positioned at Forrester Research as a DevOps Engineer II. He holds AWS, GCP, Azure, RedHat, and Kubernetes Certifications.He is a CNCF Ambassador... Read More →
Site Reliability Engineer, CNCF Ambassador, Autodesk
Meet Seema, the tech whiz at Autodesk. She's not just about fixing things – she loves sharing what she knows! Whether speaking at cool events like KubeCon NA, KubeDay, GitLab Commit, and GitHub Universe or breaking down tech on her YouTube channel, Seema makes the complicated stuff... Read More →
Wednesday June 4, 2025 11:15am - 11:55am IST Ceres
Snapshots are the backbone of data resilience and portability in OpenSearch. This session will provide a comprehensive exploration of snapshot capabilities in open-source OpenSearch, covering recent advancements and best practices for implementing robust backup, restore, and data migration strategies.
Key areas we'll delve into: 1. Fundamentals: Core architecture and mechanics of snapshot operations. 2. Repositories: Comparing storage options across file system, S3, Azure, and GCS implementations. 3. Management: Automated policy-based snapshot scheduling and lifecycle management. 4. Cross-cluster: Efficient data movement techniques for disaster recovery and migrations. 5. Searchable Snapshots: Instant access to historical data without full restoration. 6. Performance: Optimizing snapshot and restore operations for minimal cluster impact. 7. Scaling: Advanced techniques for managing snapshots in large-scale deployments. 8. Security: Repository access controls, encryption, and multi-tenant considerations. 9. Monitoring: Tools and strategies for maintaining healthy snapshot operations. Walk away with practical knowledge to master OpenSearch snapshots, from basic to advanced solutions.
Ashish Singh is a Senior Software Development Engineer at Amazon, working with the OpenSearch Service team. During his 9+ years in software development, he has focused on distributed systems, particularly OpenSearch, where he contributed to developing the remote store feature. His... Read More →
Bukhtawar Khan is a Principal Engineer working on Amazon OpenSearch Service. He is interested in building distributed and autonomous systems. He is a maintainer and an active contributor to OpenSearch.
Query Insights delivers deep visibility into search query execution, offering detailed metrics and actionable recommendations to help users optimize their search operations. This feature analyzes query characteristics and execution patterns, providing clear insights into system behavior at each processing stage. Users can proactively identify potential performance bottlenecks, implement recommended optimizations, and maintain optimal query performance. These capabilities contribute to improved system efficiency, better user experience, and increased operational reliability.
Siddhant Gupta is a Senior Product Manager (Technical) at AWS, leading OpenSearch Service development with a focus on generative AI and search technologies. With 20+ years of experience across AWS, Microsoft, and global tech companies, he specializes in cloud computing, AI/ML, and... Read More →
Wednesday June 4, 2025 12:00pm - 12:20pm IST Ceres
In this talk, we’ll share Uber’s journey in scaling OpenSearch to handle large scale data across 100+ clusters while ensuring high availability, fault tolerance, performance and zero-downtime upgrades. We’ll dive deep into our strategies for overcoming regional and zonal failures, observability and fleet-upgrade strategy with zero-downtime, at scale. Attendees will learn about our approaches to sharding, indexing, and data distribution that enables resilience across a global infrastructure. Additionally, we’ll cover key best practices for managing OpenSearch clusters at Uber’s scale, ensuring reliability, and achieving cost-efficiency.
Anurag is a Senior software engineer at Uber, working in the Search platform team. He is currently working on platformizing OpenSearch for all search workloads within Uber, focusing on reliability, speed, and efficiency in serving millions of users globally.
Currently working at Uber in Search Platform team and have been working as a software engineer for about 6 years and have experience in building backend solutions at scale.
Wednesday June 4, 2025 12:00pm - 12:40pm IST Jupiter
In the era of vector search and semantic similarity, returning highly relevant results is only half the battle. When search results are too similar, users must wade through redundant information to find diverse perspectives. This talk introduces Maximum Marginal Relevance (MMR) implementation in OpenSearch, a powerful technique that optimally balances result relevance with diversity.
Key takeaways will include: - Implementing MMR reranking with OpenSearch's vector search - Optimizing performance for large-scale deployments - Measuring and tuning diversity metrics - Real-world applications and success patterns
Joinal is an experienced Data Science professional with a interest on building solutions with quick prototypes, community engagements and influencing technology adoption.
- Why use RAG (Retrieval-Augmented Generation)? - How does OpenSearch contribute to building intelligent agents?
Barriers to Deploying RAG Systems in Production:
- Key concerns: Quality, Cost, Safety, Maintenance, and Knowledge/Time. - The biggest challenge remains quality. - What leads to quality degradation in RAG systems, and how does it happen?
Solving the Issues:
- Identifying challenges with different chunking techniques: Which one should you use, or should you develop a custom approach tailored to your needs using OpenSearch? - Addressing problems with search/querying techniques to optimize results.
By understanding these challenges and adopting the suggested solutions, you can significantly enhance the accuracy of RAG-based applications.
If needed, we can provide a deeper dive, a demo, or even hands-on workshops on this topic.
Parminder is an entrepreneur with a strong background in building and scaling businesses. In 2016, he co-founded Hansel.io, which was acquired by Netcore in 2020.In 2022, he helped launch and grow Scaler’s US operations. Currently, he is focused on building Tempera.ai, an AI company.Parminder... Read More →
Wednesday June 4, 2025 12:20pm - 12:40pm IST Ceres
In modern big data ecosystems like Databricks, logs from different jobs, stages, and components can be highly diverse, making it difficult to gain comprehensive insights into system health, performance, and operational flow. This presentation will explore how to consolidate these disparate logs into a unified OpenSearch platform, enabling advanced log analysis and real-time visibility. By leveraging large language models (LLMs) and contextual dashboards, we will demonstrate how to create a "single pane of glass" for monitoring entire operational workflows. Attendees will learn how this approach improves visibility, traceability, and transparency, enabling quicker actions and decisions based on knowledge-driven insights. Key topics include log mapping, lineage tracking, and using OpenSearch to facilitate both broad and granular insights for operational teams. By the end of this session, you'll understand how to streamline complex log data from systems like Databricks, empowering your teams with smarter, faster decision-making and more efficient troubleshooting.
Satej works as Principal Data Engineer at Zalando SE with over 14 years of experience in the industry. He has worked with renowned organizations such as Boeing, Adidas, Honeywell specializing in architecture, big data and machine learning use cases. With a strong track record of architecting... Read More →
Wednesday June 4, 2025 1:40pm - 2:20pm IST Jupiter
I’ll share how we built a highly available, multi-tenant, multi-AZ search platform using OpenSearch, supporting modern capabilities like vector search for semantic search. I’ll cover key challenges and how we ensured fault tolerance and scalability across a distributed environment. Additionally, I look forward to learning from the community’s experience in scaling OpenSearch for advanced search use cases.
Attendees will understand how to design a fault-tolerant OpenSearch architecture and integrate vector search to enhance accuracy and search performance. I’ll provide insights into optimising distributed setups for high availability and resilience, ensuring OpenSearch meets enterprise-scale demands.
By sharing best practices and challenges, I hope to drive discussions on multi-region deployments and hybrid search solutions. Highlighting OpenSearch’s capabilities for scalable, AI-powered search can help expand its adoption and evolution for next-gen search applications.
I am a seasoned search and infrastructure engineer with extensive experience in maintaining and optimizing large-scale search ecosystems. Currently, I manage 20+ OpenSearch clusters for search and vector use cases across multiple regions, ensuring 95% of search queries execute under... Read More →
Senior Staff Engineer at Freshworks with 12 years of experience specialising in the Data Platform and Observability. Expert in building observability systems, search platforms, data pipelines, and vector stores, with a strong focus on integrating AI and ML solutions. Passionate about... Read More →
During our college days, my team and I embarked on an ambitious project—a Scheme Recommendation Chatbot to help users find relevant government schemes using intelligent search. As students new to Retrieval-Augmented Generation (RAG), we faced a big challenge: finding an open-source, scalable, and efficient search solution that didn’t lock us into proprietary or managed tools.
That’s when we discovered OpenSearch—and it completely changed the game!
In this talk, I’ll share: 1. Our journey to OpenSearch and why we made the switch. 2. How we leveraged OpenSearch’s vector search to improve search accuracy. 3. Challenges we faced as students implementing OpenSearch for the first time. 4. Lessons learned and tips for others looking to integrate OpenSearch into their projects.
Now, as a professional working in the industry, I look back at how OpenSearch not only powered our chatbot but also taught us real-world skills. If you’re a student, developer, or someone curious about open-source search and relevance, this talk is for you.
Learn the story of AWS OpenSearch Service, and how we evolved the OpenSearch engine to offer high durability at Petabyte scale without sacrificing on performance. We’ll share our journey of establishing core principles and breaking down complex challenges into manageable deliverables. We will cover some of the key design decisions that helped us to allow new and prevalent software engines to co-exist, without major overhaul of the system. We will walk through our open-source first journey, illuminating common pitfalls and lessons learnt in scaling, correctness and performance, which helped shape the eventual solution. Finally, we’ll reveal how we revolutionised our periodic data backup strategy, transforming a process that handles half a million partitions into one that completes in mere seconds—enabling granular, minute-by-minute backups.
Key Take-aways - Define your tenets - Don’t try building an ideal system - Don’t re-invent the wheel - Why high judgement calls matters - Invent to simplify
Senior Software Engineer at Amazon OpenSearch Service, Amazon
Gaurav Bafna is a Senior Engineer with Amazon OpenSearch Service. He is excited about scaling challenges with distributed systems. He has been with OpenSearch team for 8 years and has a good operating experience with OpenSearch clusters. He is an active contributor and a maintainer... Read More →
Sachin is a Senior Engineer with Amazon OpenSearch Service. He has 12+ years of experience working on various distributed systems and building search solutions using Apache Solr and OpenSearch. He is a big fan of functional programming paradigm and keeps building on the side in C... Read More →
As OpenSearch continues to gain popularity for search and analytics, understanding its security landscape becomes crucial. This session introduces threat modeling for OpenSearch deployments, helping attendees identify potential vulnerabilities and implement effective mitigation strategies. We'll explore common threats, leverage the OpenSearch Security plugin, and discuss best practices for deploying opensearch clusters.
If you’re still manually sifting through logs to spot issues, it’s time to rethink your observability strategy. This session will show how AI-driven OpenSearch transforms your observability practices by turning logs, metrics, and traces into actionable insights.
Using vector and neural search, how AI models can detect patterns, anomalies, and trends in real time. Learn how to reduce troubleshooting time, predict potential issues before they become critical, and empower data-driven decisions, all while scaling your observability pipeline with OpenSearch’s powerful capabilities.
Aditya Soni is a DevOps/SRE tech professional He worked with Product and Service based companies including Red Hat, Searce, and is currently positioned at Forrester Research as a DevOps Engineer II. He holds AWS, GCP, Azure, RedHat, and Kubernetes Certifications.He is a CNCF Ambassador... Read More →
Anshika is a passionate DevOps/SRE Engineer who is always eager to learn & implement cloud-native solutions, , she has contributed to streamlining deployment processes and enhancing system reliability. She is eager to share her experiences and insights at conferences, contributing... Read More →
At Uber, OpenSearch is a core component of our search infrastructure. We manage ~100 clusters across several thousands of nodes. We platformized OpenSearch to ensure scalability and reliability for Uber’s search needs.
This talk covers:
Automated Build & Deployment: Uber maintains a custom OpenSearch fork with performance optimizations, scalability improvements, and bug fixes. We’ll discuss our build and release pipelines for deploying OpenSearch at scale.
Batch & Streaming Ingestion: We built batch ingestion using Spark and OpenSearch-Hadoop and real-time streaming with Kafka/Flink, scaling to ingest a billion vectors in X hours.
Elasticsearch to OpenSearch Migration: Insights into compatibility challenges, query performance tuning, and scaling ingestion throughput.
Security: Implementing fine-grained authentication and authorization for multi-tenant OpenSearch clusters.
Community Contributions: How we contribute optimizations, bug fixes, and features upstream to improve OpenSearch for everyone.
Vector search is a rapidly growing area within search and analytics, and the k-NN (k-Nearest Neighbors) plugin in OpenSearch offers multiple engines to support this capability, with FAISS (Facebook AI Similarity Search), which provides efficient similarity search and clustering of dense vectors, being a preferred choice.
To speed up vector operations, FAISS engine utilizes SIMD (Single Instruction Multiple Data) processing, leveraging AVX (Advanced Vector Extensions) instructions on x86 architecture. With the latest AVX-512 (512-bit wide vector processing) support in FAISS, OpenSearch achieves further performance improvements in vector indexing, quantization, and search operations.
This presentation will delve into key technical advancements, including an in-depth look at AVX, the impact of AVX-512 on vector search performance and real-world benefits for OpenSearch users. Additionally, we will discuss how these enhancements lead to better price-performance and reduced TCO (Total Cost of Ownership).
Sourav is an accomplished Engineering Manager with a strong background in Software Development, Performance Engineering, & Cloud Computing. Possessing a degree in Computer Science & Engineering, Sourav has amassed nearly two decades of professional experience, collaborating with professionals... Read More →
Abhijit Kulkarni is a proficient Software Engineer with over 14 years of experience in software design and development across diverse domains such as Payments, Telecom and Aviation. His extensive Java experience along with strong problem-solving and analytical skills have been instrumental... Read More →
Search is no longer just a feature—it’s a critical capability that drives innovation, efficiency, and decision-making across enterprises. However, scaling OpenSearch across an organization presents challenges, from infrastructure management to governance and adoption. How do you enable diverse teams—from engineering to business functions—to harness the power of OpenSearch without deep operational overhead?
This session explores how to elevate OpenSearch from a tool to an internal platform offering, allowing teams to seamlessly integrate search into their workflows. By treating OpenSearch as a self-service, scalable, and well-governed enterprise platform, organizations can unlock its potential for everything from complex knowledge engines to simpler, ad-hoc use cases.
We’ll discuss key architectural principles, governance models, automation strategies, and best practices for building an OpenSearch-powered internal platform. Whether you're dealing with enterprise-wide search initiatives, analytics, or operational insights, this talk will provide actionable strategies to ensure OpenSearch adoption is efficient, scalable, and accessible to all teams—without friction.
Operating OpenSearch in multi-tenant environments presents challenges in performance, security, and data isolation. This session explores best practices for optimizing OpenSearch for high-scale, multi-tenant architectures, covering index sharding, access control, and query performance tuning. Attendees will gain insights from real-world deployments and learn how to ensure cost efficiency and high availability in OpenSearch clusters.
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.
