Operations | Monitoring | ITSM | DevOps | Cloud

What Are Vector Embeddings? (Explained in 2 Minutes)

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.

Transform your public sector organization with embedded GenAI from Elastic on AWS

Elastic featured in AWS Generative AI Hub for public sector Elastic is proud to be featured in the new AWS Generative AI Content Hub for public sector — a destination showcasing the most impactful ways agencies can securely adopt and scale generative AI (GenAI).

How Data Ingestion Works in Elasticsearch (Quick Guide)

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.

What Are Mappings in Elasticsearch? (Explained Simply)

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.

The business impact of Elasticsearch logsdb index mode and TSDS

The Elasticsearch storage engine team has made significant strides in improving storage efficiency and performance in Elasticsearch 8.19 and 9.1. Now that these changes are available, what impact can they have on your business? And how do you make the most of them?

How Elasticsearch Works: Documents, JSON & Index Explained

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.

Elasticsearch Explained for Beginners: From Spreadsheets to JSON, Indices & Shards

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.

How Tipalti mastered Elasticsearch performance with AutoOps

From manual monitoring to proactive optimization, learn how Tipalti used AutoOps to save 10% annual costs. For a global payables automation leader like Tipalti, where financial transactions are the lifeblood of the business, infrastructure performance isn't just a technical goal; it's a core business requirement. Managing a complex ecosystem of databases, including Postgres, SQL Server, MongoDB, Kafka, and Elasticsearch, with a lean team of four engineers demands efficiency and powerful tooling.