Discover Elasticsearch’s architecture and capabilities. Learn about indexing and storing data, conducting precise queries, including fuzzy searches, and executing real-time data analysis effectively.

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This course explores Elasticsearch, an open-source, Java-based, full-text search and analytics engine that harnesses the power of the Lucene library. You’ll discover its capabilities as a versatile search engine for business data, offering the ability to store information, conduct searches on accurate and typo-ridden text, and perform real-time data analysis on vast datasets.

Throughout this course, you will gain comprehensive insights into the architecture of Elasticsearch and master the art of indexing and storing data within the Elasticsearch framework. You will progress to learn how to execute various types of queries, including fuzzy queries, and explore the intricacies of each operation and how it is implemented within Elasticsearch.

After completing the course, you’ll have gained a comprehensive understanding of the internal mechanisms of Elasticsearch and developed the skills to effectively use it for your data management and analysis requirements.

Elasticsearch Fundamentals: Indexing and Querying Data

# Overview

In Elasticsearch, **text analysis** is the process of converting unstructured text into a structured format optimized for search. This process involves tokenizing the text into individual words, filtering these tokens, and utilizing an inverted index for storage and search operations.
 
The text analysis process in Elasticsearch 
occurs at the index and search times. When indexing a text field, Elasticsearch passes it to the analyzer to perform a set of procedures/operations before storing the results in the inverted index. On the other hand, when a user searches on a `text` field, Elasticsearch again sends it to the search analyzer, which then performs a set of operations and sends the result to compare the data with the inverted index.


We will discuss some essential concepts before diving into text analysis in Elasticsearch.

# Inverted index

An inverted index is a data structure used in Elasticsearch to store and retrieve information efficiently. It maps words or terms to their location in a set of documents. The index lists all the unique words in a collection of documents and the document IDs where each word occurs. When a search query is made, the inverted index is used to quickly identify the documents that contain the query terms, allowing for fast and accurate search results. 

**Analyzers** in Elasticsearch are a series of operations, such as tokenization, filtering, stemming, normalization, and token graph, on a text field. The results of these operations are stored in the inverted index, which is then used to facilitate efficient indexing and searching. Therefore, the choice of the analyzer used can significantly impact the relevance and accuracy of the search results.





Understand the important concepts about text analysis in Elasticsearch.

Introduction to Elasticsearch

Getting started on Elasticsearch

Text Analysis

Search on ElasticSearch

Aggregation

Conclusion

Integrate Elasticsearch in the Ruby on Rails Application

What Is Text Analysis?

Overview

Inverted index