{"id":167866,"date":"2026-01-28T12:43:22","date_gmt":"2026-01-28T11:43:22","guid":{"rendered":"https:\/\/liora.io\/en\/?p=167866"},"modified":"2026-02-06T07:29:37","modified_gmt":"2026-02-06T06:29:37","slug":"elasticsearch-everything-you-need-to-know","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/elasticsearch-everything-you-need-to-know","title":{"rendered":"ElasticSearch: Everything you need to know about this software"},"content":{"rendered":"<style><br \/>\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style>\n<p><strong>Elasticsearch is a distributed open-source data search and analysis engine based on Apache Lucene and developed in Java. The project began as a scalable version of the open-source Lucene search framework. The ability to horizontally extend Lucene indices was then added.<\/strong><\/p>\nThis tool allows storing, searching, and analyzing large volumes of data quickly and in near real-time. Responses are transmitted in milliseconds.\n\nThis speed is because Elasticsearch searches an index rather than searching text directly. Its structure is based on documents rather than tables and schemas. REST APIs are used to store and explore data. In short, Elasticsearch is a server that can handle JSON queries and return JSON data.\n<h2 class=\"wp-block-heading\" id=\"h-how-does-elasticsearch-work\">How does Elasticsearch work?<\/h2>\nElasticsearch works on several basic concepts. Here are its main components.\n\nDocuments are the <b>basic unit of information<\/b> that can be indexed in Elasticsearch. It is expressed in JSON format, which is the global data interchange format.\n\nA document can be compared to a row in a <a href=\"https:\/\/liora.io\/en\/sql-learn-all-about-the-programming-language-for-databases\"><b>relational database<\/b><\/a>, representing a specific entity. However, this document is not limited to text and can be any type of <b>structured data encoded in JSON<\/b>. It can be numbers, lines of code, or dates&#8230; each document has a unique identifier and a data type describing the category of the entity it contains.\n\nAn index is a collection of documents with similar characteristics. It is the <b>highest level of an entity<\/b> on which it is possible to perform queries in Elasticsearch.\n\nYou can compare the index to <a href=\"https:\/\/liora.io\/en\/database-what-is-it\"><b>a database<\/b><\/a>. All documents in an index are linked by category. The index is identified by a name so that it can be referred to during search or analysis operations.\n\nIn reality, an Elasticsearch index is an inverted index. This mechanism is <b>the source of all search engines<\/b> and associates a mapping of content to its location in a document or set of documents. This hashmap-like data structure allows you to go from a word to a document.\n\nAn Elasticsearch cluster is a group of interconnected instances. It allows tasks, search, or indexing to be distributed between nodes.\n\nA node is an<b> individual server<\/b>, stores data and contributes to the search and indexing capabilities of the cluster. A node can be configured in different ways.\n\n<style><br \/>\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"768\" height=\"413\" src=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2023\/04\/elasticsearch-features.jpg\" alt=\"elasticsearch-features\" loading=\"lazy\" srcset=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2023\/04\/elasticsearch-features.jpg 768w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2023\/04\/elasticsearch-features-300x161.jpg 300w\" sizes=\"(max-width: 768px) 100vw, 768px\">\n\nThe Master Node controls the Elasticsearch cluster and takes responsibility for cluster-wide operations such as creating or deleting an index and adding or removing nodes.\n\nA Data Node <b>stores data and performs data operations<\/b> such as search and aggregation, while a Client Node forwards cluster queries to the Master Node and data queries to the Data Nodes.\n\nIndexes can be <b>subdivided into chunks<\/b> called &#8220;shards&#8221;. Each fragment is an independent, fully functional index that can be hosted on any node within a cluster.\n\nBy distributing the documents in an index across <b>multiple fragments<\/b> and distributing those fragments across multiple nodes, Elasticsearch provides redundancy to <b>protect against hardware failure<\/b> while increasing query capacity as nodes are added to the cluster.\n\nFinally, fragments can be copied to generate &#8220;replicas. Again, the goal is to protect data from hardware failure and increase the ability to respond to read requests.\n<h2 class=\"wp-block-heading\" id=\"h-elastic-slack\">Elastic Slack<\/h2>\nElastic Slack is a complete ecosystem of open-source tools for data ingestion, enrichment, storage, analysis, and visualization. In addition to Elasticsearch, other software includes Logstash, Kibana, and Beats.\n\nThe <strong><a href=\"https:\/\/liora.io\/en\/kibana-what-is-it\">Kibana data management<\/a><\/strong> and visualization tool deliver <b>real-time histograms<\/b>, charts, or maps. It allows you to <b>visualize Elasticsearch data in real-time<\/b>, and to choose visualizations thanks to a very intuitive interface.\n\nLogstash aggregates and processes data sent to Elasticsearch. This open-source data processing pipeline is capable of ingesting data from multiple sources, transforming it, and transferring it. Data can be transformed regardless of its format.\n\nFinally, Beats brings together several &#8220;Data Shipping&#8221; agents to send data from thousands of machines and systems to <b>Logstash<\/b> or <b>Elasticsearch<\/b>. This tool is very useful for assembling data.\n\n<img decoding=\"async\" width=\"800\" height=\"445\" src=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2023\/04\/5596347_55898-1024x569.jpg\" alt=\"Elasticsearch characteristics\" loading=\"lazy\" srcset=\"https:\/\/liora.io\/app\/uploads\/sites\/9\/2023\/04\/5596347_55898-1024x569.jpg 1024w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2023\/04\/5596347_55898-300x167.jpg 300w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2023\/04\/5596347_55898-768x426.jpg 768w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2023\/04\/5596347_55898-1536x853.jpg 1536w, https:\/\/liora.io\/app\/uploads\/sites\/9\/2023\/04\/5596347_55898-2048x1137.jpg 2048w\" sizes=\"(max-width: 800px) 100vw, 800px\">\n<h2 class=\"wp-block-heading\" id=\"h-what-is-elasticsearch-used-for\">What is Elasticsearch used for?<\/h2>\nElasticsearch is used for a wide variety of purposes. For example, Elasticsearch is used for applications that rely on a <b>search platform<\/b> to access data.\n\nWebsites that store large amounts of content also benefit from this search engine. The same is true for companies using it for internal searches.\n\nAnother use case for Elasticsearch is real-time log data ingestion and analysis, or container monitoring. In addition, this tool is widely used for <b>cybersecurity analysis<\/b>. Finally, the various features offered by the Elastic Stack make it an excellent choice for business analysis.\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/data-engineer\">Discover our Data Engineer course training<\/a><\/div><\/div>\n\n<h2 class=\"wp-block-heading\" id=\"h-by-whom-is-elasticsearch-used\">By whom is Elasticsearch used?<\/h2>\nMany companies use Elasticsearch, including some of the most well-known ones. Here are a few examples.\n\n<b>Netflix<\/b> uses the ELK stack for many use cases to <b>monitor and analyze customer service<\/b> <b>operations<\/b> and security logs. The company&#8217;s messaging system relies on Elasticsearch. While the firm initially used a few isolated deployments, it now operates one of the dozens of clusters consisting of several hundred nodes.\n\nE-commerce giant <b>eBay<\/b> has created an Elasticsearch-as-a-Service platform that allows it to <b>easily provide clusters<\/b> on its internal OpenStack-based cloud platform. This meets its text analytics and search needs.\n\nWe can also mention <b>Walmart<\/b>, the hypermarket chain. Thanks to Elastic Stack, the firm can <b>reveal the hidden value of its data<\/b> to take advantage of clues about its customers&#8217; <b>shopping habits<\/b>, the performance of its stores, or the <b>impact of seasonal events<\/b>. All in real-time. The security features of the ELK stack also help it detect any anomalies.\n<h2 class=\"wp-block-heading\" id=\"h-what-are-the-options-to-get-trained-on-elasticsearch\">What are the options to get trained on ElasticSearch?<\/h2>\nMastering ElasticSearch is a highly sought-after skill. To master this tool, you can choose Liora.\n\nOur <a href=\"\/en\/courses\/data-ai\/data-engineer\"><b>Data Engineer course<\/b><\/a> teaches you how to use Elasticsearch through the Database module. You will also learn about SQL, <a href=\"https:\/\/liora.io\/en\/mongodb-all-about-the-document-oriented-nosql-database\"><b>MongoDB<\/b><\/a>, and Neo4J.\n\nThe other modules in this program cover Python programming, Data Science, Big Data, CI\/CD, and automation. At the end of the curriculum, you will have all the skills required to become a data engineer.\n\nYou will be able to identify an organization&#8217;s data architecture needs, build acquisition and automatic processing pipelines, deploy and adapt <a href=\"https:\/\/liora.io\/en\/machine-learning-what-is-it-and-why-does-it-change-the-world\"><b>Machine Learning models<\/b><\/a> on production servers, and define a global Data strategy for the organization.\n\nThis course can be completed in a 9-month Continuing Education program, or an intensive 11-week BootCamp mode. All our distance learning courses adopt an innovative<b> Blended Learning approach<\/b>, combining individual coaching on our online platform and collective Masterclasses.\n\nAt the end of the program, you will receive a certificate issued by University La Sorbonne&nbsp; as part of a prestigious partnership.\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/data-engineer\">Start a Data Engineer training course<\/a><\/div><\/div>\n\n\nYou know everything about Elasticsearch. For more information on the Data Engineer profession, see our file on SQL.","protected":false},"excerpt":{"rendered":"<p>Elasticsearch is a distributed open-source data search and analysis engine based on Apache Lucene and developed in Java. The project began as a scalable version of the open-source Lucene search framework. The ability to horizontally extend Lucene indices was then added.<\/p>\n","protected":false},"author":85,"featured_media":167867,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"_acf_changed":false,"editor_notices":[],"footnotes":""},"categories":[2433],"class_list":["post-167866","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-ai"],"acf":[],"_links":{"self":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/167866","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/users\/85"}],"replies":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/comments?post=167866"}],"version-history":[{"count":3,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/167866\/revisions"}],"predecessor-version":[{"id":205372,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/167866\/revisions\/205372"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media\/167867"}],"wp:attachment":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media?parent=167866"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/categories?post=167866"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}