Jon Handler

AI agents are becoming your best investigators, triaging alerts, correlating telemetry, and performing root-cause analysis across metrics, logs, and traces in seconds.

When we work with information systems, we capture information in text and find information with text queries. Advances in AI have made it possible to move from word-to-word matching to something like meaning-to-meaning matching.

Join this session to discover how vector search can revolutionize your business's search capabilities compared to traditional keyword search. Vector search plays a critical role in enhancing generative AI applications and solutions by enabling more accurate and context-aware search results.

Gen AI faces adoption hurdles due to technical complexity, costs, and AI talent shortages. The Open Platform for Enterprise AI (OPEA), launched by the Linux Foundation AI & Data, addresses these issues by simplifying gen AI integration.

Making the most of your data often means using multiple AWS services, such as Amazon OpenSearch Service for full-text, geospatial, or vector search. Amazon DynamoDB offers many different ways to build integrations, including export functionality and DynamoDB Streams.

Search engines give their users a path from their goals to the information that meets those goals. When the items retrieved match the user’s intent, they are considered relevant.

Observability helps you understand what is happening in a system, often by instrumenting collections of metrics, logs, and traces. As applications grow, observability becomes more complex.

OpenSearch is a community-driven, Apache 2.0-licensed open-source search and analytics suite that makes it easy to ingest, search, visualize, and analyze data. In this workshop, deploy an OpenSearch cluster and a simple container application for searching that data.

Whether you’re searching your product catalog or storing your logs for infrastructure monitoring, application performance monitoring, or observability, Amazon OpenSearch Service is an ideal tool. Its distributed search engine scales to support high-volume ingest and query rates.

OpenSearch is an Apache 2.0-licensed tool that provides you with rich, relevant search results for your data. Paired with OpenSearch Dashboards, you can analyze and visualize your log data.

Whether you’re searching your product catalog or storing your logs for infrastructure monitoring, application performance monitoring, or observability, Amazon OpenSearch Service is the ideal tool. Its distributed search engine scales to support high-volume ingest and query rates.

There are multiple ways to migrate from Elasticsearch to OpenSearch. In this chalk talk, explore different migration approaches (for example, rolling upgrades and cluster restart upgrades), when to use them, and other considerations when migrating to OpenSearch.

Whether you have structured or unstructured data, keywords or blocks of full text, OpenSearch is a useful tool that delivers relevant search results, insights from your logs, performance for your application, and observability for your platform. In this workshop, learn how to deploy an OpenSearch cluster on Kubernetes that backs a search-based application.

Amazon Elasticsearch Service provides a near real-time solution for ingesting, analyzing, visualizing, and alerting on all of your log data. This solution can make you want to keep all of your log data, but your costs can start to grow as more data is stored.

Amazon Elasticsearch Service is a fully managed service that makes it easy for you to deploy, secure, and run Elasticsearch cost-effectively at scale. This session offers a deep dive into the service, customer use cases, best practices, and newly launched features, and it provides insight into what’s next for Amazon Elasticsearch Service.

In adopting a cloud-first strategy, Pearson had to secure and monitor its infrastructure, but with many applications deployed against many services, it needed reliable, fast ingest of logs into a real-time analytics engine. Pearson chose Amazon Elasticsearch Service for monitoring and Amazon Kinesis for ingest.

When building serverless applications, you need to know the design tradeoffs for improving performance in addition to how well your application is performing. What are the bottlenecks, time sinks, and bugs?

In adopting a cloud-first strategy, Pearson had to secure and monitor its infrastructure, but with many applications deployed against many services, it needed reliable, fast ingest of logs into a real-time analytics engine. Pearson chose Amazon Elasticsearch Service for monitoring and Amazon Kinesis for ingest.

As your application log data grows exponentially, searching and analyzing data in an automated way is essential to gathering a deep understanding of the system. Applying machine learning (ML) enables you to understand behavior patterns and identify solutions that remove bottlenecks getting in the way of processing data at scale.