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Time in Elasticsearch is more than a timestamp; it's a powerful lens. In this 90-second guide, see how Elasticsearch lets you bucket logs by minute, hour, day, or even millisecond. You can zoom out to see patterns or zoom in to investigate exact spikes, all without recalculating.

Overwhelmed by raw data? In this short video, we demonstrate how Elasticsearch utilizes buckets to group and organize data by time, value, region, or any other shared trait. Whether you're tracking error codes or hourly sales trends, buckets and nested aggregations help turn chaos into clarity. Additionally, discover how time-based bucketing enables you to spot patterns and zoom in on valuable insights quickly.

In under 2 minutes, we explain what vector embeddings are, how they work, and how to use them in real-world applications like text expansion. We'll also show how Elasticsearch supports vector search with two powerful models: E5, open-source text embedding models designed for multilingual search, and ELSER, a sparse embeddings model from Elastic.

Most people think search is only about finding results, but Elasticsearch delivers much more. In this quick guide, discover the difference between hits (the documents that match your query) and aggregations (the analytics that reveal patterns, counts, and trends beyond the results page).

Before you can search, analyze, or visualize anything in Elasticsearch, you need data ingestion. In this quick guide, we explain how data moves from raw logs, metrics, or JSON into an index using tools like Logstash, Beats, or language clients. Learn why consistency matters more than perfection and how once data is ingested, it’s ready for search, analysis, and insight.

Elasticsearch mappings turn logs from unstructured text into usable data. In this video, we explain what mappings are, how they define fields like text, number, and date, and why they matter. With the right mappings, Elasticsearch can filter error codes, sort by response time, and group results by browser, region, or version.

Ever wondered how Elasticsearch can search any kind of data? In this video, we break it down with a simple deck of cards analogy that makes indexing easy to understand. Each card is like a JSON document with fields and values, suit, color, number, type. Combine them and you’ve built an index, giving Elasticsearch the power to answer queries like “show me all the red cards” or “show me only the face cards.” If you can describe it, you can index it, and if you can index it, you can search it.

Ever wondered how Elasticsearch actually works? In this quick breakdown, I’ll use a simple spreadsheet analogy to explain the basics from documents and indices to shards, CRUD operations, and mappings. You’ll see how Elasticsearch stores data as JSON documents, splits indices into shards for scalability, uses CRUD with ID hashing for fast lookups, and applies mappings to organize text, numbers, and labels.

What is vector search? In this breakdown, learn how vector search leverages machine learning to capture the meaning and context of unstructured data by transforming it into a numeric representation that is stored in a vector database. This video also explains the difference between sparse and dense embeddings, and how vector search differs from semantic search and lexical search.

David Tippet, Search Engineer at GitHub, shares how Elastic powers GitHub’s massive search platform and enables a seamless developer experience. He explains how GitHub balances AI-driven semantic search with traditional keyword search, ensuring accuracy for millions of diverse users, from engineers to security researchers.