With digitization adopted in many industries, real-time data from manufacturing and operational equipment can be used to monitor and optimize operation - by applying data-driven modeling including machine learning. Learn how you can ingest sensor data from industrial processes and operational equipment into Elastic, build monitoring dashboards and set up automated alerts in Kibana, and apply predictive modeling to optimize your operations (OT).

Watch this video for a detailed walkthrough on instrumenting the pet clinic app with Elastic Observability''s Java APM agent on Azure App Service.

Log files and system logs have been a treasure trove of information for administrators and developers for decades. But with more moving parts and ever more options on where to run modern cloud applications, keeping an eye on logs and troubleshooting problems have become increasingly difficult. Watch this video to learn how to go beyond tail -f and process custom and unstructured logs with Elastic.

Learn how to add enriched information (new fields) to help analysis from ingested log data in Elastic Observability.

Elastic's machine learning helps support several easy-to-use features to help determine root cause analysis for logs. This includes anomaly detection and log categorization, which are easy-to-use features aiding in analysis without the need to understand or know about machine learning.

Monitoring business revenues and managing change in revenue with Elastic Machine Learning anomaly detection.

The new Elasticsearch Query Language is a flexible, powerful, and robust query expression language to interrogate data. In this session learn how ESQL provides a superior query UX, a piped query language with join capabilities that fundamentally transforms and expands the analytics and data processing of Elasticsearch.

Do you understand the differences between Elasticsearch and OpenSearch? We’ll lay them out for you. You’ll find that our take on emerging technologies is fundamentally transforming the opportunity to solve problems through search. Learn about innovation in areas like vector search and hybrid scoring or support for third-party natural language processing that help you unlock possibilities for new classes of searches through the application of machine learning. The result? Increased relevance with less burden on the developer and administrator. In this session, you'll learn all about these innovations, and how you can take advantage of them to drive success.

Finding matches when using a taxonomy is a common problem. A notable challenge is mapping a user’s query to the entity (or results) expected when searching for an entity inside a catalog mapping. Functional textual search models tend to rely on exact match or partial match, but both can lead to a frustrating experience when users aren’t familiar with the domain. Basic models often fail to support user typos, synonyms, acronyms, and/or hyponyms/hypernyms. Learn how to tackle these challenges and make search more intuitive when using a taxonomy.

Elasticsearch supports both rectangular and hexagonal grids when aggregating spatial data geographically. In this video, we compare and contrast these two approaches in Kibana using both geo_point and geo_shape data sources. To highlight this, we also make use of Kibana’s new map synchronization feature.