BEGIN:VCALENDAR
VERSION:2.0
X-WR-CALNAME:opensearchconin2026
X-WR-CALDESC:Event Calendar
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//Sched.com OpenSearchCon India 2026//EN
X-WR-TIMEZONE:UTC
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T030000Z
DTEND:20260615T040000Z
SUMMARY:Tea & Networking
DESCRIPTION:
CATEGORIES:BREAKS + NETWORKING + SPECIAL EVENTS
LOCATION:Level 2 Foyer\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:925db6dd22c97c6ac2607150e85906d8
URL:http://opensearchconin2026.sched.com/event/925db6dd22c97c6ac2607150e85906d8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T030000Z
DTEND:20260615T123000Z
SUMMARY:Registration + Badge Pick-up
DESCRIPTION:\n
CATEGORIES:REGISTRATION + BADGE PICK-UP
LOCATION:Lotus Foyer - Level 3\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:349096c4df01ba130b45e9f76cde9c29
URL:http://opensearchconin2026.sched.com/event/349096c4df01ba130b45e9f76cde9c29
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T040000Z
DTEND:20260615T050000Z
SUMMARY:Keynote Sessions To Be Announced
DESCRIPTION:\n
CATEGORIES:KEYNOTE SESSIONS
LOCATION:Lotus Ballroom 3\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:2b151e52c473ed341d629d27baae3a06
URL:http://opensearchconin2026.sched.com/event/2b151e52c473ed341d629d27baae3a06
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T050000Z
DTEND:20260615T052000Z
SUMMARY:Break
DESCRIPTION:
CATEGORIES:BREAKS + NETWORKING + SPECIAL EVENTS
LOCATION:Level 2 Foyer\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:e49327d770a6a1324e4d53ef4629a714
URL:http://opensearchconin2026.sched.com/event/e49327d770a6a1324e4d53ef4629a714
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T052000Z
DTEND:20260615T060000Z
SUMMARY:100M Logs a Day: Performance Engineering an OpenSearch Observability Platform for Kubernetes - Sravanthi Naga\, Pegasystems
DESCRIPTION:Kubernetes platforms generate massive volumes of logs from microservices\, infrastructure\, and platform services. At scale\, OpenSearch observability pipelines often struggle with shard explosion\, indexing bottlenecks\, JVM pressure\, and slow queries. This session presents a practical architecture for operating an OpenSearch observability platform handling 100M+ logs per day from Kubernetes environments. We will walk through the end-to-end pipeline—from log collection to ingestion and indexing—and share performance engineering techniques used to maintain cluster stability under heavy workloads. Topics include shard and index design\, JVM and thread-pool tuning\, optimizing indexing throughput\, and using lifecycle policies and hot-warm architectures to scale efficiently. Attendees will gain actionable strategies for building resilient OpenSearch observability platforms for cloud-native systems.
CATEGORIES:ANALYTICS + SECURITY + OBSERVABILITY
LOCATION:206\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:c5f496c342023bf04ccc9a339736bc32
URL:http://opensearchconin2026.sched.com/event/c5f496c342023bf04ccc9a339736bc32
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T052000Z
DTEND:20260615T060000Z
SUMMARY:Migrating OpenSearch To AWS Graviton — Performance\, Cost Savings\, and Strategies at Scale - Dileep Dora\, Freshworks
DESCRIPTION:We migrated ~1\,500 OpenSearch nodes across 7 clusters in 5 regions from Intel to AWS Graviton 4\, upgrading from OpenSearch 2.13 to 3.4 and JVM 17 to 21 along the way. We saw 2x indexing throughput and ~30% cost savings. We want to share the benchmarking data\, compare migration strategies (inline upgrade\, snapshot-restore\, and why CCS didn't work)\, and push more teams toward Graviton. A practical guide to evaluating and executing a Graviton migration at scale — covering instance type benchmarking (i4i/i4g/i7i/i8g)\, migration strategy trade-offs (inline upgrade is fast but risky\, snapshot-restore is safe but needs parallel infrastructure\, CCS doesn't work across major versions)\, production validation steps\, and the real performance and cost numbers from 1\,500 nodes in production. Large-scale Graviton migration data for OpenSearch is scarce. By sharing production numbers from 7 clusters across 5 regions — including what didn't work — we want to de-risk Graviton adoption for the community. The 2x throughput and 30% cost reduction we're seeing should motivate more teams to make the move\, and our migration playbook gives them a concrete path to follow.
CATEGORIES:OPERATING OPENSEARCH
LOCATION:204\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:51db50ff25bfe25057953bd9587d381b
URL:http://opensearchconin2026.sched.com/event/51db50ff25bfe25057953bd9587d381b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T052000Z
DTEND:20260615T060000Z
SUMMARY:Beyond Top-K: Building Search-Confidence Guardrails for Agentic AI With OpenSearch - Chanpreet Singh & Shatakshi Pandey\, Amazon
DESCRIPTION:Large Language Model agents frequently rely on retrieval systems to make decisions\, yet most pipelines still depend on a naive top-k retrieval strategy. In agentic workflows this can lead to a dangerous failure mode: agents confidently acting on low-relevance results\, amplifying hallucinations through iterative tool use.\n \n This session presents a practical framework for implementing search-confidence guardrails using OpenSearch. Instead of treating retrieval scores as opaque signals\, we demonstrate how to convert heterogeneous ranking outputs into a normalized trust metric that agents can reason about.\n \n We will explore techniques such as score normalization\, Reciprocal Rank Fusion (RRF)\, and hybrid retrieval (BM25 + vector search) to build a deterministic confidence layer on top of OpenSearch queries. Using this signal\, agents can dynamically decide whether to answer\, re-query\, or request clarification\, preventing cascading hallucinations.\n \n The talk includes a reference architecture for agent-search interaction\, evaluation workflows using the OpenSearch comparison tooling.
CATEGORIES:SEARCH & APACHE LUCENE
LOCATION:205\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:5cde66e4fa77fc3cf632b017add43eb4
URL:http://opensearchconin2026.sched.com/event/5cde66e4fa77fc3cf632b017add43eb4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T061000Z
DTEND:20260615T063000Z
SUMMARY:OpenSearch for Association Rule Mining: With ActivityWatch - Indrajith Ekanayake\, Informatics Institute of Technology
DESCRIPTION:This session is about the recent productivity study we ran in our open source lab (OSL). I’ll walk through the end-to-end architecture: extracting device telemetry\, transforming it into task sessions\, and running association rule mining on top of OpenSearch to compute support and confidence for goal-relevant activity patterns. The entire setup runs locally\, therefore no risk of activity data theft. ActivityWatch is cross platform open-source time tracker that collects telemetry on how we spend time on devices. It comes with watchers that can do all the data collection from AFK to browser windows. In our setup\, ActivityWatch runs on each device\, and OpenSearch is self-hosted on our lab’s local LAN (I'm academic)\, and then we ingest logs into it every 10 seconds using API-based ingestion. While ActivityWatch runs\, users tag their intended task (e.g.\, #learn\, #java). We align tags with telemetry windows\, sessionize events into transactions (items[]=apps/domains\, duration)\, and mine rules with support/confidence/lift per tag. If the current window drifts from the active tag with high confidence for a short period\, we send a nudge reminding they are distracted from the original goal.
CATEGORIES:ANALYTICS + SECURITY + OBSERVABILITY
LOCATION:206\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:b7f64f38832f7ad71e3c0569c7cb96ee
URL:http://opensearchconin2026.sched.com/event/b7f64f38832f7ad71e3c0569c7cb96ee
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T061000Z
DTEND:20260615T063000Z
SUMMARY:Live Queries in OpenSearch: Real-Time Search Observability From Zero To Production - Kishore Kumaarn Natarajan\, Amazon Opensearch
DESCRIPTION:When a search cluster slows down\, the first question is: what's running right now? Until recently\, OpenSearch had no answer. Top N Queries shows what was slow after the fact\, but by then the root cause may have disappeared. In this talk\, I'll walk through Live Queries\, a feature I built for the Query Insights plugin that gives operators real-time visibility into every in-flight and recently completed search request across the cluster. I'll cover: - Why "what's running now?" was unanswerable and how operators struggled with manual correlation of node stats and thread dumps - The end-to-end architecture: REST handler → transport fan-out → TaskGroup collection → LiveQueryRecord assembly with per-shard task details (CPU\, memory\, running time) - The lock-free Finished Queries Cache: ConcurrentLinkedDeque with CAS-based lazy activation achieving zero hot-path overhead - Correlating live and finished queries via nodeId:taskId keys linked to Top N records - Evolution from OpenSearch 3.0 (inflight API) through 3.1 (cancellation tracking) to 3.3 (WLM group filtering) - Live demo and future roadmap: comprehensive latency breakdown (RFC #20693) and OpenTelemetry integration
CATEGORIES:OPERATING OPENSEARCH
LOCATION:205\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:03b23f46d0ec9d1ae76422460628e2d5
URL:http://opensearchconin2026.sched.com/event/03b23f46d0ec9d1ae76422460628e2d5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T061000Z
DTEND:20260615T063000Z
SUMMARY:Offline Batch Ingestion for OpenSearch: Distributed Indexing Reducing Cluster Impact - Tarun Kishore & Monika Agarwal\, Uber
DESCRIPTION:Large-scale ingestion can significantly impact OpenSearch cluster stability\, query latency\, and operational efficiency. Traditional bulk APIs and Spark/Hadoop connectors rely on online indexing\, where clusters must handle indexing and queries simultaneously—creating resource contention and long ingestion windows. This session introduces an Offline Batch Ingestion framework that builds OpenSearch-compatible Lucene segments outside the cluster using distributed Spark executors. The system leverages OpenSearch’s native indexing engine and snapshot/restore APIs to deploy indexes atomically with zero production impact. We’ll cover the architecture (distributed indexing\, shard merge\, snapshot generation)\, technical implementation using OpenSearch Engine APIs\, production learnings\, scalability characteristics\, and future extensions such as vector indexing. This approach decouples indexing compute from serving clusters\, enabling faster ingestion\, safer reindexing\, and improved operational resilience.
CATEGORIES:SEARCH & APACHE LUCENE
LOCATION:204\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:7f1ff2377d5649201881841a4fd797ac
URL:http://opensearchconin2026.sched.com/event/7f1ff2377d5649201881841a4fd797ac
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T064000Z
DTEND:20260615T070000Z
SUMMARY:From Blindspots To Bottlenecks: Deep Search Analysis at Your Fingertips - David Zane & Chenyang Ji\, AWS
DESCRIPTION:Search performance directly impacts user experience and system resilience. OpenSearch Query Insights provides unprecedented visibility into query execution with minimal performance overhead.\n \n This presentation explores Query Insights capabilities and demonstrates how to identify performance bottlenecks\, diagnose issues in real-time\, and optimize your cluster. Through live demos\, you'll see how to pinpoint the top queries consuming resources\, understand query patterns for structural improvements\, and leverage dashboards for data-driven optimization. Whether you're managing a small cluster or large-scale search platform\, you'll gain the visibility needed to keep OpenSearch running at peak efficiency.\n \n Key Features to Demo:\n - Real-time query monitoring and live execution tracking\n - Resource consumption rankings (CPU\, memory\, latency)\n - Advanced query grouping and pattern detection\n - Interactive Query Insights Dashboard with filtering and drill-down\n - Query metadata and node/shard-level insights\n - Historical query analysis and trend tracking\n - Performance metrics export and integration capabilities
CATEGORIES:ANALYTICS + SECURITY + OBSERVABILITY
LOCATION:204\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:6e2c7f971904ad9cf5995e61218316e2
URL:http://opensearchconin2026.sched.com/event/6e2c7f971904ad9cf5995e61218316e2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T064000Z
DTEND:20260615T070000Z
SUMMARY:Operating OpenSearch at Scale: Fixing Hot Shards\, Disk Imbalance\, and Cluster Instability - Dhruvan Tanna\, Verve & Aditya Krishnakumar\, SentinelOne
DESCRIPTION:Running OpenSearch in production at scale is very different from what tutorials or books show. When you manage many clusters\, you start seeing issues you didn’t know existed\, like shard and disk imbalance\, uneven traffic distribution\, and unstable cluster states. These problems can degrade search performance and even cause incidents. In this session\, we will share real challenges we faced while operating large OpenSearch clusters and the practical solutions we used to stabilize them. We will explore how shard distribution can silently create problems\, why some nodes end up using much more disk than others\, and how clusters behave under heavy indexing and query load. This talk focuses on the operational side of running OpenSearch in production. We’ll discuss strategies for better shard allocation\, preventing disk imbalance\, controlling indexing pressure\, and keeping clusters stable under load. Attendees will leave with practical techniques they can apply to run OpenSearch clusters reliably at large scale and improve stability and performance in real-world environments.
CATEGORIES:OPERATING OPENSEARCH
LOCATION:205\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:df49f99f71c6108ce0a97f42c534ef41
URL:http://opensearchconin2026.sched.com/event/df49f99f71c6108ce0a97f42c534ef41
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T064000Z
DTEND:20260615T070000Z
SUMMARY:OpenSearch for Indian Languages: A Tamil Search Engine Case Study - Achanandhi M\, EY & Tamil Vanan Karuppannan\, Arcesium
DESCRIPTION:Most search systems are designed primarily for English. But what happens when we want search to truly understand regional languages like Tamil? Tamil is one of the world’s oldest living languages with a rich literary tradition\, yet building effective search for it comes with unique challenges such as complex morphology\, script handling\, tokenization\, and language-specific relevance tuning. In this talk\, we share how we built an intelligent Tamil search system using OpenSearch. Using Tirukkural\, the classic Tamil text of 1\,330 couplets\, as a practical dataset\, we explore how OpenSearch can be adapted to understand Tamil queries and return meaningful results. We’ll walk through practical techniques such as custom analyzers\, tokenization strategies\, stemming approaches\, and relevance tuning for Tamil search. We’ll also demonstrate how users can search Kurals using natural Tamil phrases and still discover the most relevant verses. Beyond Tamil\, these techniques can help developers build better search experiences for many regional Indian languages bridging ancient knowledge with modern search technology.
CATEGORIES:SEARCH & APACHE LUCENE
LOCATION:206\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:52a736884e166d2432ab23db1ab8f581
URL:http://opensearchconin2026.sched.com/event/52a736884e166d2432ab23db1ab8f581
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T071000Z
DTEND:20260615T075000Z
SUMMARY:Agent Under the Microscope: Monitoring Agentic Workflows With OpenSearch - Shenoy Pratik Gurudatt\, Anirudha Jadhav & Megha Goyal\, AWS OpenSearch
DESCRIPTION:AI agents are moving from demos to production\, but observability hasn't kept up. When an agent takes a wrong path\, hallucinates mid-task\, or silently degrades\, how do you investigate? Traditional APM treats agent execution as a black box. We need purpose-built\, OpenTelemetry-native observability for agentic AI.\n \n We introduce the Agent Traces and Agent Health for OpenSearch: a native UI for exploring agent execution traces. OTel SDKs with GenAI semantic conventions (gen_ai.* attributes) instrument your agents\, Data Prepper ingests the spans\, and Agent Traces show you hierarchical trace views\, detail agent maps\, and aggregate metrics like token usage and latency percentiles - all queryable via PPL.\n \n We demonstrate root-cause investigation: expanding execution trees to inspect each LLM call and tool invocation\, querying spans to answer "which tool call caused the agent to diverge?" We then go deeper with Agent Health's golden path comparison that evaluates trajectories against expected behavior. Whether you're building agents for customer support\, code generation\, or data pipelines\, you'll leave with a practical playbook for agent observability.
CATEGORIES:ANALYTICS + SECURITY + OBSERVABILITY
LOCATION:205\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:78abe9ba9e11b021893001fcfb48c45b
URL:http://opensearchconin2026.sched.com/event/78abe9ba9e11b021893001fcfb48c45b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T071000Z
DTEND:20260615T075000Z
SUMMARY:Beyond Keywords: OpenSearch as the Context Store for Local SRE Agents - Abhinav Sharma\, KodeKloud & Jatin Sharma\, Independent
DESCRIPTION:Traditional keyword search is no longer sufficient for modern SRE workflows because infrastructure error messages are often too generic to be actionable. This session explores a technical shift in observability: moving from standard indexing to high-dimensional vector embeddings for terminal traces and post-mortems using OpenSearch.\n \n We will deep-dive into a privacy-first AI architecture\, demonstrating how to integrate OpenSearch’s vector engine with local\, private models (such as Llama) to ensure sensitive production logs never leave your VPC.\n \n You will learn the mechanics of building a Retrieval-Augmented Generation (RAG) pipeline designed specifically for infrastructure telemetry. The session includes a live demo where a local agent "recalls" the exact historical fix for a messy\, real-time stack trace\, providing a direct link to the relevant PR or configuration change from the past.
CATEGORIES:OPERATING OPENSEARCH
LOCATION:206\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:4fa2804cf8ff8ee17a2fe642ce4c46cc
URL:http://opensearchconin2026.sched.com/event/4fa2804cf8ff8ee17a2fe642ce4c46cc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T071000Z
DTEND:20260615T075000Z
SUMMARY:OpenSearch Vector DB Best Practices - Jon Handler\, AWS
DESCRIPTION:Vector search in OpenSearch offers a rich set of configuration options\, but choosing the right combination of engine\, algorithm\, quantization\, and storage tier can be daunting — especially as your workload grows or shifts from semantic search to agentic AI. This session covers both workload types at small\, medium\, and high scale. You'll learn how to apply the cost-recall-latency curve to choose between Faiss and Lucene\, HNSW and IVF\, and the right quantization technique for your budget and recall requirements. You'll explore tiered storage from in-memory to disk-based and memory-optimized\, and production tuning techniques including bulk indexing strategies\, GPU-accelerated builds\, and automated parameter optimization. You'll leave with best practices to apply whether you're running thousands of vectors on a single node or billions across a fleet.
CATEGORIES:SEARCH & APACHE LUCENE
LOCATION:204\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:944b0e689b2a1456ba9f9d80b2a89cf5
URL:http://opensearchconin2026.sched.com/event/944b0e689b2a1456ba9f9d80b2a89cf5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T075000Z
DTEND:20260615T090000Z
SUMMARY:Lunch
DESCRIPTION:
CATEGORIES:BREAKS + NETWORKING + SPECIAL EVENTS
LOCATION:Level 2 Foyer\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:902a601590546d1930f2fa81bd98008d
URL:http://opensearchconin2026.sched.com/event/902a601590546d1930f2fa81bd98008d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T090000Z
DTEND:20260615T092000Z
SUMMARY:High Availability and Disaster Recovery Strategies for OpenSearch Clusters - Rishav Kumar & Ganesh Bombatkar\, Amazon
DESCRIPTION:This session explores building and maintaining highly available OpenSearch clusters with robust disaster recovery strategies. We'll examine critical components of resilient OpenSearch infrastructure\, focusing on multi-node cluster design across availability zones and effective backup solutions. The presentation covers essential aspects of shard allocation\, replica configuration\, and cross-cluster replication for continuous operation during failures. Learn practical approaches to automated failover\, snapshot management\, and monitoring strategies for optimal cluster health. Through real-world examples and lessons learned from large-scale implementations\, we'll discuss common pitfalls and best practices for both cloud and on-premises deployments. This talk is designed for OpenSearch administrators\, DevOps engineers\, and architects managing mission-critical search infrastructure.\n \n Key Takeaways:\n \n -Understanding HA architecture patterns and DR strategies\n -Implementing effective backup and recovery solutions\n -Monitoring and maintaining cluster health\n -Real-world examples and practical demonstrations\n -Best practices for different deployment scenarios
CATEGORIES:ANALYTICS + SECURITY + OBSERVABILITY
LOCATION:206\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:74518cd97ce09833491d3179fdf7f1f3
URL:http://opensearchconin2026.sched.com/event/74518cd97ce09833491d3179fdf7f1f3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T090000Z
DTEND:20260615T092000Z
SUMMARY:OpenSearch Vs. The Unknown: Real-Time Threat Hunting on a Budget - Prerit Munjal\, Groupon
DESCRIPTION:Security isn't just about firewalls and alerts\, it’s about curiosity\, context\, and catching the weird stuff before it gets weird. But let’s be real: most teams don’t have the budget (or patience) for a million-dollar SIEM. In this talk\, I’ll walk you through how we turned OpenSearch into a scrappy\, surprisingly powerful threat-hunting platform. Using native tools\, open-source plugins\, and a healthy dose of creativity\, we built real-time alerting and investigation flows without blowing up costs. We'll cover: • Designing log schemas that highlight anomalies. • Building threat-detection pipelines using ingest processors and OpenSearch Dashboards. • Real-life incident where OpenSearch helped us catch something our cloud provider missed. If you’ve ever felt like security tools are either overkill or underwhelming\, this session is for you. You’ll walk away with practical patterns and open-source recipes for turning OpenSearch into your security command center — no license key required.
CATEGORIES:ANALYTICS + SECURITY + OBSERVABILITY
LOCATION:204\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:a6bb85ff97cf2b6a8bd321a27b255903
URL:http://opensearchconin2026.sched.com/event/a6bb85ff97cf2b6a8bd321a27b255903
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T090000Z
DTEND:20260615T092000Z
SUMMARY:The Secret Life of a Search Query: A Fun\, Visual Journey Through How OpenSearch Really Thinks - Shubhi Khanna\, Independent
DESCRIPTION:Ever wondered what really happens after you hit “Search”? This talk takes you on a fast\, funny\, and surprisingly eye‑opening journey through the secret life of a query inside OpenSearch. We’ll follow it as it squeezes through analyzers\, hops across shards\, meets vectors\, dodges caches\, and races toward the perfect answer\, all in milliseconds. Along the way\, you’ll discover why some queries feel instant\, why others take a coffee break\, and how tiny architectural choices can totally change the search experience. Whether you're new to OpenSearch or a seasoned engineer\, you’ll walk away with a delightful mental model of how search really works and how to make it faster\, smarter\, and a whole lot more magical.
CATEGORIES:OPERATING OPENSEARCH
LOCATION:205\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:61f2880937f63b9c2b6fba16cdfa852b
URL:http://opensearchconin2026.sched.com/event/61f2880937f63b9c2b6fba16cdfa852b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T093000Z
DTEND:20260615T101000Z
SUMMARY:From Queries To Insights: AI-Powered Database Monitoring With OpenSearch and Prometheus - Shenoy Pratik Gurudatt & Anirudha Jadhav\, AWS OpenSearch
DESCRIPTION:Databases are the backbone of every application\, yet monitoring them effectively remains a challenge. In this session\, we walk through monitoring production databases powering an e-commerce platform Valkey for caching and PostgreSQL for the catalog deployed on Kubernetes & Docker\, using OpenSearch and Prometheus as the observability backbone.\n \n We demonstrate a complete OpenTelemetry-based pipeline: instrumenting both databases with OTel SDKs\, routing telemetry through Data Prepper to store time-series metrics in Prometheus and traces/logs in OpenSearch. With AI-assisted investigation using text-to-PPL and text-to-PromQL\, engineers query live telemetry in natural language asking "show me slow queries in the catalog service in the last hour" without writing complex syntax.\n \n We also show how to use AI to generate RCA visualizations and surface performance insights across your database fleet. Whether you manage a handful of databases or hundreds\, you'll leave with practical patterns for building an OpenTelemetry-native database observability stack.
CATEGORIES:ANALYTICS + SECURITY + OBSERVABILITY
LOCATION:206\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:225ec51aabcb9e776a566a5142b53848
URL:http://opensearchconin2026.sched.com/event/225ec51aabcb9e776a566a5142b53848
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T093000Z
DTEND:20260615T101000Z
SUMMARY:The Leapfrog Migration Playbook: Escaping Proprietary Search Without Breaking Production - Sagar Utekar\, CrowdStrike & Sakshi Nasha\, Cohesity
DESCRIPTION:Most teams have a dirty secret: they're running Elasticsearch versions they can't afford to upgrade and can't afford to stay on. The migration feels impossible due to data loss risk\, query regression\, extended downtime\, and no rollback. So they wait. And wait. This talk ends the waiting. We walk through the OpenSearch Migration Assistant end-to-end: a fully open-source toolkit that handles metadata migration\, historical backfill via Reindex-from-Snapshot\, and live traffic capture + replay so your users never feel the move. We'll cover all three migration scenarios\, the trickiest mapping transformation pitfalls (dense_vector → knn_vector\, anyone?)\, and live comparative response diffing before the final cutover. Migrate smarter\, not harder.
CATEGORIES:OPERATING OPENSEARCH
LOCATION:204\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:0aec8d00da19c767a934afcfa602ecef
URL:http://opensearchconin2026.sched.com/event/0aec8d00da19c767a934afcfa602ecef
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T093000Z
DTEND:20260615T101000Z
SUMMARY:Architecting a Sub-200ms Product Discovery System for Ecommerce With OpenSearch and Lucene - Kartik Sapra\, Uber & Naman Jain\, CNCF
DESCRIPTION:Modern ecommerce search systems must do more than return results. They must understand customer intent\, retrieve relevant products\, and do all of this within extremely tight latency budgets. Even small delays can impact user experience and conversion. In this talk\, I will walk through the architecture of a product discovery system designed to deliver relevant product suggestions in under 200 milliseconds using OpenSearch and Lucene. The session will explore how search infrastructure can power real time product discovery across high traffic ecommerce platforms. We will cover how queries are processed\, how candidate products are retrieved efficiently\, and how filtering and ranking strategies help surface high quality and buyable items. The talk will also discuss practical techniques for managing latency budgets\, optimizing search queries\, and designing indexes that balance speed and relevance. Attendees will gain practical insights into building fast and scalable product discovery systems and learn how OpenSearch and Lucene can be used to power low latency search experiences in modern ecommerce applications.
CATEGORIES:SEARCH & APACHE LUCENE
LOCATION:205\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:bb83055c137072629077f7f5989a4e95
URL:http://opensearchconin2026.sched.com/event/bb83055c137072629077f7f5989a4e95
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T102000Z
DTEND:20260615T110000Z
SUMMARY:PPL Power-Up: Advanced Data Transformation Pipelines for Observability at Scale - Bharav Patel\, AWS
DESCRIPTION:Piped Processing Language (PPL) has quietly become one of OpenSearch's most powerful tools for observability data transformation. OpenSearch 3.5 added various powerful functions that unlock advanced use cases: join \, Lookup\, mvcombine\, mvzip\, mvfind\, and mvmap for multivalue field operations\, addtotals for instant summary tables\, and streamstats for cumulative statistical calculations as events are processed. This session showcases these capabilities through real-world observability scenarios — correlating multivalue log fields across microservices\, building running error rate dashboards with streamstats\, and performing lightweight anomaly detection using just PPL with no ML model required. We'll also demonstrate cross-signal analysis by combining PPL log queries with Prometheus metric data using the new Discover experience for Prometheus data sources shipped in 3.5. Attendees will leave with ready-to-use PPL patterns for incident investigation that are more intuitive than equivalent SQL approaches.
CATEGORIES:ANALYTICS + SECURITY + OBSERVABILITY
LOCATION:205\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:fbf6b9715d94c3916165f2192ed198c2
URL:http://opensearchconin2026.sched.com/event/fbf6b9715d94c3916165f2192ed198c2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T102000Z
DTEND:20260615T110000Z
SUMMARY:Generative Discovery on OpenSearch: Intent\, Context\, Cognition - Rajani Maski\, Shutterstock
DESCRIPTION:Search finds. Discovery reveals. At Shutterstock\, serving one of the world's largest licensed creative content libraries at 2000+ requests per second\, the difference is everything. Generative Discovery changes the contract entirely. It treats every interaction as a signal of intent\, builds context across modalities\, and applies cognition to surface what users did not know to ask for. This talk introduces a production architecture built around three principles: Intent: multimodal signals including text\, image\, and behavioral context unified into rich intent representations driving OpenSearch k-NN and hybrid retrieval. Context: session aware\, editorially grounded RAG pipelines using OpenSearch as a dynamic retrieval backbone at scale. Cognition: generative agents that think\, orchestrate multi step retrieval\, and resolve ambiguous intent\, treating OpenSearch as an intelligent reasoning substrate rather than a passive index. You will learn from real production decisions\, evolving thinking\, and lessons still being learned on the frontier of Generative Discovery.
CATEGORIES:OPERATING OPENSEARCH
LOCATION:206\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:d9f28e72136a1d9592c00cef401937b9
URL:http://opensearchconin2026.sched.com/event/d9f28e72136a1d9592c00cef401937b9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T102000Z
DTEND:20260615T110000Z
SUMMARY:From Search To Answers: Building Agentic\, Multi-Modal RAG Platform With OpenSearch - Naresh Waswani\, Simpplr Inc. & Jyoti Notani\, Persistent Sysytems Ltd
DESCRIPTION:Enterprise search is evolving from keyword-based retrieval into intelligent\, context-aware systems powered by generative AI. While many RAG examples focus on simple vector lookups\, building a production-grade\, multi-modal RAG platform requires more—especially at enterprise scale. This talk presents a reference architecture for agent-assisted\, multi-modal RAG systems with OpenSearch as the core retrieval and indexing layer. OpenSearch combines lexical relevance\, vector similarity\, and metadata filtering across text\, images\, audio\, and video\, while preserving deterministic control over retrieval in AI-driven workflows. The session covers ingestion pipelines\, chunking strategies\, hybrid and multi-vector indexing\, and retrieval orchestration\, and explains how OpenSearch acts as the retrieval intelligence boundary between probabilistic agent reasoning and enterprise data. Key production trade-offs around relevance tuning\, multi-tenant isolation\, performance scaling\, and cost control are also discussed\, providing practical guidance for building reliable\, enterprise-ready AI search platforms.
CATEGORIES:SEARCH & APACHE LUCENE
LOCATION:204\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:6c687716ae1ca993ab1b0eed1c5418f9
URL:http://opensearchconin2026.sched.com/event/6c687716ae1ca993ab1b0eed1c5418f9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T110000Z
DTEND:20260615T112000Z
SUMMARY:Break
DESCRIPTION:
CATEGORIES:BREAKS + NETWORKING + SPECIAL EVENTS
LOCATION:Level 2 Foyer\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:1309131e275048b0e4fb57c2faab3b61
URL:http://opensearchconin2026.sched.com/event/1309131e275048b0e4fb57c2faab3b61
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T112000Z
DTEND:20260615T114000Z
SUMMARY:AI Investigation Agents for Debugging Live Incidents Using OpenSearch - Aman Kimothi\, Ashish Gupta & Jai Mashalkar\, Oracle Cloud Infrastructure
DESCRIPTION:Debugging production incidents in distributed systems often means manually searching through thousands of log lines to understand what went wrong. During high-impact outages\, engineers must quickly correlate signals across services\, identify error patterns\, and reconstruct the sequence of events.\n \n In this talk\, we explore how AI investigation agents can assist engineers by using OpenSearch as the investigation backbone for incident analysis. Instead of simply retrieving logs\, an agent can iteratively query OpenSearch\, identify dominant error patterns\, correlate events across services\, and build a timeline of failures before producing a concise root-cause explanation.\n \n We will demonstrate a lightweight investigation agent diagnosing a simulated microservices failure using logs stored in OpenSearch. Attendees will see how engineers can ask questions like “Why did the ingestion service fail?” and watch the agent autonomously investigate and explain the incident.\n \n Participants will leave with a practical architecture for building AI-assisted debugging workflows on top of OpenSearch.
CATEGORIES:ANALYTICS + SECURITY + OBSERVABILITY
LOCATION:206\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:a72bd311521dc9d16090f96ae193861e
URL:http://opensearchconin2026.sched.com/event/a72bd311521dc9d16090f96ae193861e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T112000Z
DTEND:20260615T114000Z
SUMMARY:From Code To Carbon - GreenOps for OpenSearch: Building Cost-Efficient and Sustainable Tech - Amrutha KH\, Kantata
DESCRIPTION:As data volumes and AI-driven workloads grow\, operating large-scale search and observability platforms comes with rising infrastructure costs and energy consumption. GreenOps\, an approach that combines operational efficiency\, cost management\, and environmental responsibility\, is emerging as a practical way for engineering teams to run cloud systems more sustainably. This session explores how GreenOps principles apply when operating OpenSearch for search and observability workloads. We will examine strategies to optimise cluster sizing\, storage\, and indexing patterns to reduce unnecessary compute usage while maintaining performance and reliability. The talk will also cover ways teams can use observability data to identify inefficient workloads\, control infrastructure costs\, and reduce the overall carbon footprint of their systems. Attendees will gain practical insights into running more efficient OpenSearch deployments while aligning operational decisions with broader sustainability and cost-optimization goals.
CATEGORIES:OPERATING OPENSEARCH
LOCATION:205\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:5086430de465c80cfdc72565550424d0
URL:http://opensearchconin2026.sched.com/event/5086430de465c80cfdc72565550424d0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T115000Z
DTEND:20260615T123000Z
SUMMARY:The Black Box for AI Agents: Observability\, Memory & Coordination With Strands and OpenSearch - Hitesh Subnani & Smita Singh\, AWS
DESCRIPTION:Agentic AI systems are moving from experiments to production—but most teams lack visibility into how agents\n think\, coordinate\, and evolve. This session explores using OpenSearch as the cognitive backbone for modern agent\n frameworks like Strands and LangGraph\, positioning it as: a long-term memory store\, reasoning trace index\, multi-\n agent coordination layer\, and observability platform.\n \n Through a live architecture walkthrough\, we'll build a multi-agent system where every planning step\, tool\n invocation\, state transition\, and outcome is indexed in OpenSearch. We'll demonstrate how to debug hallucinations\n using reasoning traces\, replay agent decisions across sessions\, analyze performance with hybrid search\, detect\n behavioral drift\, and coordinate multiple agents through search-backed state.\n \n Attendees will gain practical architectural patterns for running stateful\, observable\, production-grade AI agents\n using OpenSearch as core infrastructure—beyond basic retrieval.\n \n Key Takeaways:\n • Design stateful agent memory architectures\n • Implement agent observability pipelines\n • Use OpenSearch for reasoning trace analysis\n • Coordinate multi-agent systems via indexed state
CATEGORIES:ANALYTICS + SECURITY + OBSERVABILITY
LOCATION:205\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:ba1f081e2dc16b8c4b6c5bab125f6381
URL:http://opensearchconin2026.sched.com/event/ba1f081e2dc16b8c4b6c5bab125f6381
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T115000Z
DTEND:20260615T123000Z
SUMMARY:Private LLMs With OpenSearch Agents - Abdul Muneer Kolarkunnu\, NetApp InstaClustr & Rudraksh Karpe\, Simplismart
DESCRIPTION:This OpenSearchCon talk covers setting up and enabling agents for private environments\, from initial configuration to solving common challenges. We faced numerous issues connecting OpenSearch Agents to our private LLM. Through trial and error\, we succeeded by setting certificates in the JDK keystore\, enabling private MCPs\, and more. We identified gaps in OpenSearch and addressed them via pull requests\, including creating a blueprint for Ollama and adding an option to disable certificate validation for LLM connectors. In this session\, we'll share our journey\, discuss use cases\, and provide a live demo using the latest OpenSearch features\, including the brand new Agentic UI. You'll leave with practical insights to configure and optimize OpenSearch Agents for private LLMs\, solutions to common integration issues\, and the knowledge to deploy customized\, fully private search experiences.
CATEGORIES:OPERATING OPENSEARCH
LOCATION:204\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:72095fbb55e58bdd159e3666bf30aa73
URL:http://opensearchconin2026.sched.com/event/72095fbb55e58bdd159e3666bf30aa73
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T115000Z
DTEND:20260615T123000Z
SUMMARY:Inside the OpenSearch Query Execution Pipeline - Samyuktha M S\, IBM
DESCRIPTION:Search engines are widely used but rarely understood at a deeper level. What actually happens between the moment a user submits a query and the results appear on the screen?\n This session provides a technical deep dive into the OpenSearch query execution pipeline\, exploring how user queries are processed\, distributed\, and ranked across a cluster. The talk begins with how text is analyzed and indexed using Lucene\, including tokenization\, analyzers\, and inverted indexes. It then walks through how queries are parsed\, executed across shards\, and scored using relevance algorithms such as BM25.\n Participants will also learn how OpenSearch coordinates distributed search\, merges shard results\, and optimizes performance through caching and query planning. Finally\, we will examine debugging tools and profiling techniques that help developers understand and tune search relevance and performance.\n By the end of the session\, attendees will have a deeper understanding of the internals of OpenSearch and how those internals influence search quality and system performance.
CATEGORIES:SEARCH & APACHE LUCENE
LOCATION:206\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:a3ac4fb729afe9b1468dfcfea5c888e5
URL:http://opensearchconin2026.sched.com/event/a3ac4fb729afe9b1468dfcfea5c888e5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260615T123000Z
DTEND:20260615T140000Z
SUMMARY:Search Party
DESCRIPTION:
CATEGORIES:BREAKS + NETWORKING + SPECIAL EVENTS
LOCATION:Level 2 Foyer\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:134684ebc2c3ff8c42b853a6d4ac119a
URL:http://opensearchconin2026.sched.com/event/134684ebc2c3ff8c42b853a6d4ac119a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260616T030000Z
DTEND:20260616T040000Z
SUMMARY:Tea & Networking
DESCRIPTION:
CATEGORIES:BREAKS + NETWORKING + SPECIAL EVENTS
LOCATION:Level 2 Foyer\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:aa7bc1be89d34b6a72526b7f1306c259
URL:http://opensearchconin2026.sched.com/event/aa7bc1be89d34b6a72526b7f1306c259
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260616T030000Z
DTEND:20260616T063000Z
SUMMARY:Registration + Badge Pick-up
DESCRIPTION:
CATEGORIES:REGISTRATION + BADGE PICK-UP
LOCATION:Level 2 Foyer\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:7a2480983b4e3e1c71e20f1722fbc91d
URL:http://opensearchconin2026.sched.com/event/7a2480983b4e3e1c71e20f1722fbc91d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260616T040000Z
DTEND:20260616T042000Z
SUMMARY:From Plugin To Platform — Building an Observability Suite in OpenSearch Dashboards - Harikrishnan Prabhakar & Ganesh Gopal\, Freshworks
DESCRIPTION:OpenSearch Dashboards plugins are often used to add individual features\, but how far can the plugin model go? In this talk we share how we took the dashboards-observability plugin skeleton and evolved it into a full observability platform supporting tracing\, RUM\, profiling\, synthetics\, model observability\, alerting\, AI-powered RCA\, and pipeline monitors — all within a single plugin. We’ll walk through the architectural patterns behind this evolution\, including domain-based state management\, lazy-loaded modules\, custom clients\, and config-driven feature toggling. We’ll also discuss integrating rich visualizations such as trace timelines\, dependency graphs\, and flamegraphs into the Dashboards plugin shell\, extending the query grammar for syntax highlighting\, and organizing server routes with a middleware layer to proxy multiple backends. Attendees will leave with practical patterns for building complex applications on OpenSearch Dashboards and a deeper understanding of how the plugin architecture can evolve from simple extensions into an extensible application platform for full solutions.
CATEGORIES:ANALYTICS + SECURITY + OBSERVABILITY
LOCATION:204\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:eaf124ad529b5263f07079d2e355996d
URL:http://opensearchconin2026.sched.com/event/eaf124ad529b5263f07079d2e355996d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260616T040000Z
DTEND:20260616T042000Z
SUMMARY:HSplit: Teaching OpenSearch To Split Smart\, Not Split Hard - Shaik Subhani & Atri Sharma\, Apple
DESCRIPTION:Operating OpenSearch at large scale with multi-tenant\, hierarchical data introduces serious sharding challenges. Queries can fan out to hundreds of shards\, hot shards reduce throughput\, and rebalancing large clusters becomes operationally difficult. This session introduces HSplit\, a production-proven intelligent sharding system designed for OpenSearch deployments managing millions of folders\, billions of documents\, and petabytes of data. HSplit analyzes hierarchical structure\, access-control patterns\, and usage behavior to automatically determine optimal partition boundaries. We’ll show how HSplit reduced per-query shard fanout from 200+ shards to single digits (95% reduction) and improved P95 latency from 2–5 seconds to 100–300 ms (10× faster) while maintaining stable performance as data grows. The session covers access-aware partitioning\, composite scoring strategies\, constraint-driven splitting\, and stateless routing that supports thousands of queries per second. Attendees will gain practical strategies\, architectural patterns\, and decision frameworks for designing efficient sharding strategies for large-scale\, multi-tenant OpenSearch environments.
CATEGORIES:OPERATING OPENSEARCH
LOCATION:205\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:0c8a4f1471ee8f908319994b71a66c5b
URL:http://opensearchconin2026.sched.com/event/0c8a4f1471ee8f908319994b71a66c5b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260616T040000Z
DTEND:20260616T042000Z
SUMMARY:Building a Secure Enterprise RAG Assistant With OpenSearch Vector Search - Kartik Sapra\, Uber & Naman Jain\, CNCF
DESCRIPTION:Organizations generate huge amounts of internal documentation across wikis\, runbooks\, architecture guides\, and operational documents. Finding the right information quickly can be difficult when traditional keyword search cannot capture the intent behind a question. In this talk\, I will share how a Retrieval Augmented Generation assistant can be built using OpenSearch vector search to retrieve relevant knowledge from enterprise documentation. The system converts user queries into embeddings\, retrieves the most relevant documents from OpenSearch\, and provides them to a language model to generate grounded answers. The session will walk through the architecture of the system\, including document ingestion\, embedding generation\, vector indexing\, and semantic retrieval. We will also discuss strategies for improving retrieval quality\, managing latency in the query pipeline\, and ensuring that enterprise data remains secure. Attendees will learn how OpenSearch can power scalable semantic search systems and how vector search can be combined with language models to build reliable knowledge assistants for modern engineering organizations.
CATEGORIES:SEARCH & APACHE LUCENE
LOCATION:206\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:681fe609eec02067019f8ea7e2419bef
URL:http://opensearchconin2026.sched.com/event/681fe609eec02067019f8ea7e2419bef
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260616T043000Z
DTEND:20260616T045000Z
SUMMARY:From OpenTelemetry To OpenSearch: Designing a Scalable Observability Pipeline - Bikram Debnath\, IBM India Software Labs (ISL)
DESCRIPTION:Modern distributed applications generate massive volumes of telemetry in the form of logs\, metrics\, and traces. Designing a scalable pipeline to collect\, process\, and analyze this data is essential for effective observability.\n \n In this session\, we explore how OpenTelemetry and OpenSearch can be combined to build a modern observability pipeline. We walk through the end-to-end telemetry flow - from instrumenting applications with OpenTelemetry SDKs to collecting and processing telemetry through the OpenTelemetry Collector and finally indexing and analyzing data in OpenSearch.\n \n We also discuss key design considerations such as telemetry ingestion patterns\, index strategies for observability workloads\, and correlating logs\, metrics\, and traces. Attendees will see how OpenSearch can act as a scalable backend for storing\, searching\, and analyzing observability data.\n \n Audience Takeaway:\n Learn how to design a scalable observability pipeline using OpenTelemetry for telemetry collection and OpenSearch for storage and analysis\, along with key considerations for ingesting and correlating logs\, metrics\, and traces.
CATEGORIES:ANALYTICS + SECURITY + OBSERVABILITY
LOCATION:206\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:570fe9851344846ee00d31ba379afcf7
URL:http://opensearchconin2026.sched.com/event/570fe9851344846ee00d31ba379afcf7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260616T043000Z
DTEND:20260616T045000Z
SUMMARY:From Signals To Security: Automation & Observability in the Modern Cloud - Rahul Bhalla\, IBM India Pvt. Ltd.
DESCRIPTION:Open source observability has moved beyond dashboards and “logs vs metrics vs traces.” Today\, teams are converging on unified telemetry pipelines that support reliability and security use cases—without locking into a single tool. In this session\, we’ll map the evolving landscape of open source tools for analytics\, observability\, and security\, and show how modern stacks are built around standardized instrumentation (OpenTelemetry)\, scalable backends (Prometheus ecosystem\, Grafana/Loki/Tempo\, OpenSearch/Elastic alternatives)\, and policy-driven operations in Kubernetes. We’ll cover the biggest shifts shaping the ecosystem: tool sprawl → composable platforms\, manual triage → automated correlation\, and rising data costs → smarter sampling and routing. You’ll leave with a practical reference architecture for collecting\, enriching\, routing\, storing\, and querying telemetry—plus guidance on selecting components based on scale\, compliance\, and budget.
CATEGORIES:ANALYTICS + SECURITY + OBSERVABILITY
LOCATION:205\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:52eebf0b633f9e1164669d6a313b894e
URL:http://opensearchconin2026.sched.com/event/52eebf0b633f9e1164669d6a313b894e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260616T043000Z
DTEND:20260616T045000Z
SUMMARY:From Keyword Hell To Semantic Heaven: Building Intelligent Search With OpenSearch Vector Search - Karan Yadav\, Microsoft
DESCRIPTION:Your users search for “comfortable shoes for standing all day” and get shoe polish. 30% of searches return zero results even when products exist. This is keyword hell\, costing you customers daily. This session reveals how we transformed Adobe Commerce search using OpenSearch’s vector search capabilities. Learn the complete technical journey\, including text embeddings\, k-NN similarity\, and hybrid search that combines keyword precision with semantic understanding. We’ll cover the architecture\, including vector indexing with FAISS and Lucene\, ingest pipelines for automatic embedding generation\, and query-time optimization. Honest challenges included computational costs\, embedding model selection through sentence-transformers comparisons\, dimension tradeoffs between 384 and 768\, cold start problems\, and explainability to stakeholders. You’ll get actionable knowledge such as exact OpenSearch configurations\, PPL queries for semantic search\, hybrid scoring strategies\, open-source model recommendations\, and cost optimization for Indian startups\, with $30 per month infrastructure compared to $200 or more for managed vector databases.
CATEGORIES:SEARCH & APACHE LUCENE
LOCATION:204\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:86706a69fb67230aae8138e0230b2f96
URL:http://opensearchconin2026.sched.com/event/86706a69fb67230aae8138e0230b2f96
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260616T050000Z
DTEND:20260616T054000Z
SUMMARY:How Search Infrastructure Powers Modern AI Assistants and Knowledge Systems - Priyanshi Omer\, AWS & Neel Shah\, StackGen
DESCRIPTION:Modern AI assistants and knowledge systems rely heavily on high-quality retrieval to provide accurate and contextual responses. While large language models generate answers\, it is the underlying search infrastructure that retrieves relevant information and grounds AI outputs in real data. In this session\, we explore how modern AI applications use search infrastructure as the backbone of their intelligence layer. We will examine the architectural patterns that combine OpenSearch with embeddings\, vector search\, and hybrid retrieval to power knowledge assistants\, enterprise search systems\, and AI copilots. The talk will walk through the evolution of search infrastructurfrom traditional keyword-based search to semantic retrieval and retrieval-augmented generation (RAG). We will discuss key design considerations such as document chunking strategies\, embedding pipelines\, hybrid ranking approaches\, and relevance tuning required to build reliable AI systems. Attendees will also learn how it enables scalable retrieval pipelines that support real-time indexing\, vector similarity search\, and metadata filtering—capabilities that are essential for powering AI-driven knowledge systems at scale.
CATEGORIES:ANALYTICS + SECURITY + OBSERVABILITY
LOCATION:204\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:019e9772bdc2d8b08437e739a41c43e4
URL:http://opensearchconin2026.sched.com/event/019e9772bdc2d8b08437e739a41c43e4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260616T050000Z
DTEND:20260616T054000Z
SUMMARY:Scaling Vector Search With GPU Acceleration on OpenSearch 3.0 - Chintan Agrawal\, Amazon Web Service
DESCRIPTION:Building billion-scale vector indexes on OpenSearch has traditionally meant days of CPU-intensive processing. OpenSearch 3.0 changes this with GPU-accelerated index building using CAGRA and auto-optimize for automated hyperparameter tuning. This session shares hands-on lessons building billion-scale vector indexes on OpenSearch 3.0. We cover the end-to-end journey: selecting embedding models\, choosing between HNSW and IVF algorithms\, leveraging GPU-accelerated builds for up to 9x faster indexing\, and using auto-optimize to balance recall\, latency\, and cost without manual tuning of ef_construction and M parameters. We dive into production realities including quantization strategies (scalar\, product\, binary) to reduce memory footprint\, disk-optimized vector search for cost-sensitive workloads\, and hybrid search combining vector similarity with BM25 keyword matching. We cover operational patterns for zero-downtime index rebuilds and monitoring vector search performance. Attendees leave with a decision framework for vector index configuration\, benchmarks across deployment sizes\, and guidance on when GPU acceleration pays off versus CPU-only approaches.
CATEGORIES:OPERATING OPENSEARCH
LOCATION:205\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:6aa1baf623a8ad0cb72211773ca304c5
URL:http://opensearchconin2026.sched.com/event/6aa1baf623a8ad0cb72211773ca304c5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260616T050000Z
DTEND:20260616T054000Z
SUMMARY:Performance Improvement: Lucene Bulk Collection and gRPC Search in Action - Abdul Muneer Kolarkunnu\, NetApp InstaClustr & Sakshi Nasha\, Cohesity
DESCRIPTION:Description Join us to explore the latest performance improvements in OpenSearch\, highlighting the integration of Lucene’s bulk collection API and enhancements to the gRPC Search API. This session will dive into how bulk collection optimizes aggregation execution\, delivering measurable gains across analytical workloads by batching operations and reducing computational overhead. We will also cover the expanded features of the gRPC Search API in OpenSearch 3.4\, which now supports new query types\, improved bulk requests\, and multiple document formats. To make these concepts clear and practical\, we will showcase live demos illustrating performance boosts in aggregation workloads and expanded query handling through gRPC. Attendees will gain a clear understanding of how these advancements significantly enhance efficiency and broaden query capabilities in real‑world use cases. Key takeaways include understanding Lucene’s bulk collection benefits\, learning about new gRPC query types\, and discovering improvements in bulk request handling and document format support.
CATEGORIES:SEARCH & APACHE LUCENE
LOCATION:206\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:3c673d07c25e6036697d655ef317d704
URL:http://opensearchconin2026.sched.com/event/3c673d07c25e6036697d655ef317d704
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260616T054000Z
DTEND:20260616T060500Z
SUMMARY:Break
DESCRIPTION:
CATEGORIES:BREAKS + NETWORKING + SPECIAL EVENTS
LOCATION:Level 2 Foyer\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:e6ce964c9141a574b6291b4666cc87a9
URL:http://opensearchconin2026.sched.com/event/e6ce964c9141a574b6291b4666cc87a9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260616T060500Z
DTEND:20260616T064500Z
SUMMARY:From Noisy Logs To Actionable Insights: AI-Assisted Observability With Fluent Bit\, OpenSearch & RAG - Jeevitha G\, Juniper Networks
DESCRIPTION:Modern Kubernetes platforms generate massive volumes of logs\, yet most teams still rely on keyword searches and dashboards to debug production issues. This slows down incident response and hides valuable operational knowledge inside unstructured data. In this talk\, we’ll demonstrate how to build an AI-assisted observability pipeline using Fluent Bit\, OpenSearch\, and Retrieval-Augmented Generation (RAG) to transform raw logs into queryable\, contextual insights. We’ll walk through a cloud-native architecture that streams logs from Kubernetes workloads\, enriches and indexes them in OpenSearch\, and uses RAG to answer operational questions in real time. Attendees will see how this approach helps SREs and platform teams reduce MTTR\, improve root-cause analysis\, and move from reactive troubleshooting to proactive observability—using open-source\, CNCF-aligned tooling.
CATEGORIES:ANALYTICS + SECURITY + OBSERVABILITY
LOCATION:204\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:1cf19c313f19aed8f2f5ded4dcd0c14f
URL:http://opensearchconin2026.sched.com/event/1cf19c313f19aed8f2f5ded4dcd0c14f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260616T060500Z
DTEND:20260616T064500Z
SUMMARY:Securing Code at Scale: AI-Powered Security Analytics With OpenSearch and Agentic Workflows - Hitesh Subnani\, AWS & Bhoomika Manghwani\, SupplyHouse
DESCRIPTION:This session explores how to build a comprehensive code security platform using OpenSearch's powerful analytics capabilities enhanced with AI agentic workflows. I'll demonstrate how to implement a system that continuously monitors\, analyzes\, and remediates security vulnerabilities across your codebase. Attendees will learn how to leverage OpenSearch for storing and analyzing code security telemetry\, while implementing agentic AI workflows that autonomously identify patterns\, prioritize vulnerabilities\, and suggest remediation strategies. Key topics include: • Configuring OpenSearch for efficient storage and querying of code security data • Implementing vector search to identify similar vulnerability patterns across repositories • Building AI agents that autonomously scan\, analyze\, and classify security issues • Creating agentic workflows that coordinate specialized security analysis tasks • Developing automated remediation suggestions using LLM-powered agents • Visualizing security insights through OpenSearch Dashboards This session provides a practical blueprint for security teams looking to scale their code security.
CATEGORIES:ANALYTICS + SECURITY + OBSERVABILITY
LOCATION:206\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:fbd5c7324e46ab13511c1444263eaac7
URL:http://opensearchconin2026.sched.com/event/fbd5c7324e46ab13511c1444263eaac7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260616T060500Z
DTEND:20260616T064500Z
SUMMARY:Building Context-Aware AI Agents With Persistent Memory in OpenSearch and Claude - Shubham Kumar & Ramya Bhat\, Amazon Web Services
DESCRIPTION:AI agents powered by LLMs such as Claude are inherently stateless and rely on external systems to maintain persistent memory. While these models can reason\, plan\, and call tools\, they cannot retain knowledge across sessions on their own\, forcing users to repeat information and limiting context-aware interactions. In this session\, we explore how to build context-aware AI agents with persistent memory using OpenSearch. Attendees will learn how OpenSearch’s agentic memory enables agents to store conversations\, extract knowledge\, learn user preferences\, and retrieve relevant context over time. Through practical examples\, we demonstrate semantic knowledge extraction\, preference learning\, and session summarisation\, showing how models like Claude can leverage stored context for personalised\, intelligent responses. By the end\, participants will understand how to design scalable memory architectures that transform stateless LLM agents into context-aware systems that continuously learn from interactions.
CATEGORIES:OPERATING OPENSEARCH
LOCATION:205\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:ae4d9d12da679871f14285d31e4d5433
URL:http://opensearchconin2026.sched.com/event/ae4d9d12da679871f14285d31e4d5433
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260616T065500Z
DTEND:20260616T071500Z
SUMMARY:Observability for AI Agents: Using OpenSearch To Debug Autonomous Systems - Pushkar Mishra\, OpenText
DESCRIPTION:Modern AI agents generate massive operational telemetry: prompts\, tool executions\, policy decisions\, and execution outcomes. Without strong indexing and analytics\, debugging and governing these systems becomes extremely difficult. This talk presents a practical architecture for operating autonomous systems at scale using OpenSearch as the observability and decision-intelligence backbone. We explore an AgentOps architecture where OpenSearch indexes agent traces\, policy decisions\, incident telemetry\, and execution evidence to enable real-time debugging\, safety enforcement\, and auditability. Topics covered include building searchable “decision traces” for AI agents\, designing observability pipelines for autonomous systems\, using OpenSearch for policy audits and incident forensics\, and scaling analytics for multi-agent operations. Attendees will learn concrete patterns for using search and analytics infrastructure to make AI-driven systems observable\, debuggable\, and governable in production.
CATEGORIES:ANALYTICS + SECURITY + OBSERVABILITY
LOCATION:204\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:b6e1fd36b6c49d23f792fa87ba82e93d
URL:http://opensearchconin2026.sched.com/event/b6e1fd36b6c49d23f792fa87ba82e93d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260616T065500Z
DTEND:20260616T071500Z
SUMMARY:One Click To Observe Them All: Auto-Instrumenting LLMs With OpenTelemetry and OpenSearch - Aditya Soni\, SailPoint & Anshika Tiwari\, Amazon Web Services
DESCRIPTION:Large Language Models (LLMs) power some of today’s most advanced AI applications-from chatbots to intelligent copilots-yet monitoring their complex behavior remains a major challenge. This session demonstrates how OpenTelemetry’s auto-instrumentation capabilities\, combined with OpenSearch’s powerful analytics\, provide end-to-end observability for LLM-based systems.\n \n You’ll learn how to automatically collect rich telemetry data-traces\, metrics\, and logs-from your LLM applications with minimal manual effort using OpenTelemetry and libraries like OpenLIT. See how this data flows into OpenSearch\, enabling real-time analysis\, anomaly detection\, and performance monitoring tailored specifically for AI workloads.\n \n By the end of this talk\, you will get the tools and best practices to build scalable\, AI-driven observability pipelines that keep your LLM applications reliable and performant.
CATEGORIES:ANALYTICS + SECURITY + OBSERVABILITY
LOCATION:206\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:dc3b2455b4f228c15f80ea72a6bbd3de
URL:http://opensearchconin2026.sched.com/event/dc3b2455b4f228c15f80ea72a6bbd3de
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260520T014846Z
DTSTART:20260616T065500Z
DTEND:20260616T071500Z
SUMMARY:Searching System Behavior Instead of Logs - Samarth Sharma\, DataGenie
DESCRIPTION:Modern observability tools mostly index log lines\, metrics\, and traces as isolated events. But production incidents rarely happen because of a single event. They happen because of patterns of behavior across services. In this session\, I’ll explore a different approach: indexing system execution paths instead of raw logs. Rather than storing individual log lines\, request traces are converted into behavioral documents that represent how a request actually moved through the system. This makes it possible to search for things like unusual request flows\, repeated failure paths\, or rare service interactions. We’ll walk through: => Transforming distributed traces into searchable execution paths => Designing an index structure for behavioral search => Querying for patterns like repeated timeout paths or rare service flows => How OpenSearch scoring can highlight abnormal request behavior A small demo will simulate a microservice environment and show how OpenSearch can surface failure patterns by searching system behavior instead of individual log lines.
CATEGORIES:SEARCH & APACHE LUCENE
LOCATION:205\, Mumbai\, Maharashtra\, India
SEQUENCE:0
UID:329a95ed871c62d7fb4ee47fc0af7159
URL:http://opensearchconin2026.sched.com/event/329a95ed871c62d7fb4ee47fc0af7159
END:VEVENT
END:VCALENDAR