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

A **built-in analyzer** in Elasticsearch is a preconfigured set of rules and algorithms that combines character filters, tokenization, and token filters to process and analyze text data. These analyzers can be used without the need for creating or configuring a custom analyzer. 

Elasticsearch offers a variety of built-in analyzers that facilitate the processing and analysis of text data stored in its indexes. Here is a list of the commonly used built-in analyzers:









- Standard analyzer
- Whitespace analyzer
- Keyword analyzer
- Fingerprint analyzer
- Language analyzer


The **standard analyzer** is the default analyzer used in Elasticsearch, and it divides the text into individual tokens whenever it encounters a non-letter. In addition, it removes punctuation, lowercases the terms, and supports eliminating stop words (such as `a` and  `the`).

> **Note:** The standard analyzer uses the Unicode Text Segmentation algorithm to tokenize the input text.

For example, if the text `"The Fast fox is running."` is analyzed by the standard analyzer, it will produce the following tokens after lowercasing terms and removing punctuation: 
```
["the", "fast", "fox", "is", "running"]
```


The **whitespace analyzer** splits the text into terms whenever it encounters any whitespace character.

For example,  a whitespace analyzer will produce the following tokens from the text `"QUICK Brown-Foxes"`: 

```
["QUICK", "Brown-Foxes"]
```

Explore the most commonly used built-in analyzers and how to use them.

Introduction to Elasticsearch

Getting started on Elasticsearch

Text Analysis

Search on ElasticSearch

Aggregation

Conclusion

Integrate Elasticsearch in the Ruby on Rails Application

Built-In Analyzers

Overview

Standard analyzer

Whitespace analyzer