OpenTelemetry’s mission is to enable effective observability with high-quality, portable, and ubiquitous telemetry. The Elastic Stack's native OTEL support now aligns directly with that goal, making it easier and more economical to adopt OTel without vendor-specific agents or SDKs.

This talk reviews how Observability data signals are generated in software systems, the benefits and shortcomings of OpenTelemetry 3rd party integrations, and advancements in grassroots first-party OpenTelemetry instrumentation. We’ll end with a short demo showing how to enable effective observability with OpenTelemetry and the ElasticStack.

Panel discussion on AI in the enterprise codebase.

For Security Operations Center leaders, the daily reality is a battle against alert fatigue. IUniderstand how you can build security operations to detect, investigate, and respond to threats at the speed and scale of cloud. Learn about AI-powered Attack Discovery which automatically surfaces high-fidelity threats, Elastic Workflows, a native automation engine, and Elastic AI Agent Builder to investigate and monitor using out-of-the-box AI agents or to build custom agents.

Discover how Elasticsearch’s powerful vector database capabilities and the robust Azure AI Foundry Agent Framework combine to power smarter agents. View how to synthesize information from diverse data sources and how to use A2A and MCP for orchestrating complex tasks. Learn how to design and apply intelligent agents for scalable information retrieval, task coordination, and integration across systems.

Streams provides a single, centralized UI within Kibana that streamlines common tasks like extracting fields, setting data retention, and routing data using AI, so you don't need to use multiple applications or manually configure underlying Elasticsearch components.

Abstract: We added on-the-fly gzip decompression to Elastic Filebeat and the Elastic Agent—our log collection tools—to enable the ingestion of gzip archives and rotated logs. A performance drop was expected, so we benchmarked the feature only to find that the performance didn't drop at all. This talk is the story of our hunt for a non-existent bottleneck and how a holistic view of application performance uncovered the surprising truth about where the real costs lie.

Arno will explore the evolution of search technology in the age of AI. From large language models and “LLM Wars” to enterprise-scale challenges in observability and security, he’ll share practical insights on how Elastic customers are experimenting with AI, what works today, and why the answer often depends on context.

L'introduction de ES|QL dans Elasticsearch facilite la recherche et l'analyse de grands jeux de données.\n\nES|QL présente ses résultats sous forme tabulaire en JSON, CSV et aussi au format Apache Arrow, un format de dataframe compact permettant des échanges sans désérialisation, qui est nativement supporté par la librairie Python Pandas.\n\nCette intégration ouvre de nouvelles perspectives pour l'exploration des données avec les outils habituels des data analysts, et l'intégration facile des pipelines d'aggrégation dans les applications.\n\nAprès un bref aperçu de ES|QL, nous ferons une exploration interactive d'un jeu de données avec ES|QL, Arrow et Pandas dans un notebook Jupyter. Et un petit benchmark vous montrera l'efficacité du format Arrow comparé à JSON !

Découvrez comment la création de divers projets parallèles a révélé le besoin d'un outil plus performant et sécurisé pour interagir avec Elasticsearch. Explorez avec nous le processus qui nous a amenés à choisir Rust pour son potentiel en termes de performance et de sécurité. Ce talk présente un POC (Proof of Concept) illustrant comment ces projets parallèles ont inspiré et façonné sa création. Nous examinerons un écosystème riche, les défis rencontrés et les solutions innovantes mises en œuvre pour aboutir à un outil robuste.

For years, Elasticsearch was known as a powerful engine for traditional text ingestion, processing, and search - for example, logs. In this talk, we’ll cover the capabilities introduced since then that make Elasticsearch (and the rest of the Elastic Stack) a strong choice for going beyond log analysis and classic BM25 search. Topics include dense vectors, sparse vectors, and hybrid search, along with features that improve effectiveness in modern retrieval scenarios.

Demonstration of building an agentic AI application to support financial analysts with a conversational AI assistant, including architectural components (Anthropic Claude 3.5 Sonnet, Amazon Bedrock, Elasticsearch Vector Database, Elasticsearch MCP Server) and capabilities such as pattern identification, linking news sentiment to portfolio performance, and real-time natural language data engagement.

Scaling Agentic AI with Claude, MCP, and Vectors. We'll focus on a financial services Agentic AI case study that empowers analysts with a conversational AI assistant built using Anthropic Claude 3.5 Sonnet on Amazon Bedrock. Elasticsearch vector database. Elasticsearch MCP (Model Context Protocol) Server. This assistant transforms complex workflows—like assessing the impact of market news on thousands of customer portfolios—into an intuitive, natural language dialogue. We'll demonstrate how to build and deploy AI Agents that help: Rapidly identify patterns in complex financial data; Build meaningful correlations, such as linking news sentiment to portfolio performance; Engage with your data in real-time, using natural language. We'll also highlight how MCP servers can integrate additional services, such as weather data and email notifications, demonstrating the power of search and generative AI.

In a world flooded with data, dashboards alone aren't enough—organisations need real-time answers that drive action. Auror, a leading retail crime intelligence platform, leverages Elastic’s AI-powered search to unify and analyse data at scale—accelerating investigations, enabling cross-organisational collaboration, and significantly reducing retail shrink. In this session, discover how search-native architecture empowers decision intelligence, operational resilience, and frontline impact—delivering measurable ROI and strategic business value.

Discover how Elastic Cloud Serverless and Google Vertex AI empower the creation of AI-driven search applications with effortless scalability. This session explores Elastic's intuitive serverless architecture and dynamic scaling, integrating with Google Vertex AI to create world class search experiences. Learn how this powerful partnership simplifies deployments and accelerates innovation for modern search, observability, and security workloads.

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Unlock the true potential of your enterprise data with AI agents that transcend chat. This panel explores how leading companies build production-ready AI agents that deliver real-world impact. We’ll examine Google Cloud, MongoDB, Elastic, and open source tools, including generative AI and large language model (LLM) optimization with efficient data handling. Learn practical approaches and build the next wave of AI solutions.

Maximize performance with innovations in Compute Engine. Come learn about the latest innovations and portfolio additions from Compute Engine. Learn about new virtual machines (VMs) that are purpose-built to deliver leadership performance for all your workloads, including AI and machine learning (ML), databases, enterprise applications, and network and security compliances. Understand how to pick the right VM. We’ll cover product capabilities, best practices, and announce exciting new products targeted at making it easy for you to operate your Compute Engine environment.

Recognized by Gartner as a leading observability tool, Elasticsearch is not just log analytics. It has infrastructure monitoring, alerts, APM capabilities—and it's all open-source! Now with the addition of OpenTelemetry, it's even easier to onboard your telemetry data in a standard and vendor-neutral way. Join Andrzej in a technical session to discover the shortest path from zero to a fully functional open-source observability solution with the OTEK stack - OpenTelemetry, Elasticsearch and Kibana.

ES|QL is a new piped query language for Elasticsearch. It supports writing composable queries and it features a multi-staged execution. Unlike the other languages supported by Elasticsearch, ES|QL doesn't transpile to Query DSL or use the internal search client: it's based on its own stack. This comes with a sophisticated query analysis and optimisation steps, as well as parallelisation and vectorisation. This talk will give an overview of the execution flow of a query and touch on a few key implementation aspects, following the query from its first syntactic analysis down to Lucene delegation followed by returning the results back to the user, all in a distributed environment.

Elastic on Google Cloud allows you to modernize your AI-powered search experiences, predictively find and resolve problems, and protect against cyber threats. Learn how to derive actionable intelligence through AI-driven insights to get the most out of your data and infrastructure using the Elastic AI Assistants for Observability and Security. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

Click the blue “Learn more” button above to tap into special offers designed to help you implement what you are learning at Google Cloud Next 25.

Elastic on Google Cloud allows you to modernize your AI-powered search experiences, predictively find and resolve problems, and protect against cyber threats. Learn how to derive actionable intelligence through AI-driven insights to get the most out of your data and infrastructure using the Elastic AI Assistants for Observability and Security. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

Click the blue “Learn more” button above to tap into special offers designed to help you implement what you are learning at Google Cloud Next 25.

Elasticsearch and Kibana added a brand new query language: ES|QL — coming with a new endpoint (_query) and a simpler syntax. It lets you refine your results one step at a time and adds new features like data enrichment and processing right in your query. And you can use it across the Elastic Stack — from the Elasticsearch API to Discover and Alerting in Kibana. But the biggest change is behind the scenes: Using a new compute engine that was built with performance in mind. Join us for a quick overview and a look at syntax and internals.

Elastic on Google Cloud allows you to modernize your AI-powered search experiences, predictively find and resolve problems, and protect against cyber threats. Learn how to derive actionable intelligence through AI-driven insights to get the most out of your data and infrastructure using the Elastic AI Assistants for Observability and Security. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

Click the blue “Learn more” button above to tap into special offers designed to help you implement what you are learning at Google Cloud Next 25.