Operations | Monitoring | ITSM | DevOps | Cloud

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.

In this session, we’ll show you how *hybrid search using Elastic* lets you assign weights to different search types — for example, giving semantic search three times more influence than lexical search. This lets you fine-tune the balance between precise keyword matching and broader, context-aware results.

Ever wondered how Wikipedia loads answers instantly? Or how does your Uber update in real-time? That’s Elastic Search working behind the scenes. In this video, I break down how Elastic powers lightning-fast, scalable search for complex data from ride requests to stock prices.

In this session, we’ll show you how Elastic's ELSER takes a single token like _“Terminator”_ and expands it into semantically related terms such as _software, alien, computer technology,_ and _Connor_ (for John Connor). This makes search results more relevant, even when the exact keyword isn’t used.

What is semantic intent, and why is it crucial in the age of *AI search?* In this episode of Quick Question, we break down how semantic *intent interprets* the meaning behind your query in semantic search. About Elastic Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale. Elastic’s solutions for search, observability, and security are built on the Elastic Search AI Platform — the development platform used by thousands of companies, including more than 50% of the Fortune 500.

In this video, I’ll explain what semantic search is and how it’s different from traditional keyword search. I’ll walk you through the limitations of lexical search, what we mean by semantic intent, and how vector search plays a role under the hood. About Elastic Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale. Elastic’s solutions for search, observability, and security are built on the Elastic Search AI Platform — the development platform used by thousands of companies, including more than 50% of the Fortune 500.