Learn about Elasticsearch and why it is used.

What is Elasticsearch?

Elasticsearch is an open-source search engine that provides the ability to store, search, and analyze business data. Whether this data is textual, numerical, geospatial, structured, or unstructured, we can refer to Elasticsearch as a NoSQL database because it stores data in an unstructured (JSON) way. Still, unlike most NoSQL databases, Elasticsearch focuses more on search capability and the ability to analyze data using aggregation queries.

Elasticsearch was developed using the Java programming language with the help of the Lucene library. It contains built-in RESTful APIs that help send and respond to requests.

Why use Elasticsearch?

Firstly, the most important feature that Elasticsearch provides is the ability to efficiently apply full-text search on a large volume of data, as compared to relational and non-relational databases, which do not provide an efficient way to use this search technique.

Full-text search is a technique that searches for documents or databases, not only for titles but also for content, and returns the result based on a score that attaches to each document. For example, if we search for the word “science” in an article dataset, the full-text search will return articles that contain the word “science,” words that have a similar meaning to “science,” and articles that contain words closely matching it.

Here are other reasons for using the Elasticsearch datastore:

  • It has a high-performance, scalable, and distributed architecture that helps perform speedy search requests on a large volume of data.

  • Elasticsearch allows us to search for various data types such as textual, numerical, geospatial (GeoQuery), structured, and unstructured data types.

  • Elasticsearch provides aggregation requests that help us explore trends and patterns in our data.

  • It provides the ability to handle typos in the search.

Uses of Elasticsearch

Elasticsearch is mainly used for the following two reasons:

  • Search: Elasticsearch’s full-text search functionality makes it an excellent choice for many companies to use it as a search engine. Elasticsearch can be used to power searches on websites and perform a Google-like search for a website’s content. Companies like Amazon, GitHub, and Stack Overflow use Elasticsearch on their platform to power their search results. Additionally, Elasticsearch provides a feature called fuzzy search that allows us to find an instance that approximately matches the searched text. This helps Elasticsearch handle user typos in the search.

  • Analytics: Elasticsearch can perform real-time analytics on large volumes of data. It offers aggregation capabilities, such as histograms, statistical summaries, and geospatial analysis, which can be used to extract insights from data.

Many companies use Elasticsearch for search and analytic tasks. Here are some of them:

  • LinkedIn: LinkedIn uses Elasticsearch to index and search its extensive database of user profiles and provide real-time analytics for its professional networking platform.

  • Netflix: Netflix uses Elasticsearch to power its recommendation engine and provide real-time analytics for its streaming service.

  • Wikipedia: Wikipedia uses Elasticsearch to index and search its vast collection of articles and provide real-time website analytics.

  • Adobe: Adobe uses Elasticsearch to index and search customer data and provide real-time analytics for its marketing and customer experience management platforms.

  • Datadog: Datadog is a company that provides a platform for monitoring and analytics. It uses Elasticsearch to store and analyze metrics data, such as performance and system metrics, and provide real-time analytics capabilities.

Who is this course for?

This Elasticsearch course is designed for anyone interested in learning how to use Elasticsearch for search and analytics purposes. The course is suitable for beginners with no prior knowledge of Elasticsearch, as well as experienced developers and system administrators looking to enhance their skills.

There are no specific prerequisites for this course, apart from some basic programming knowledge. Additionally, familiarity with databases and SQL would be beneficial, but it is optional. The course will cover all the necessary concepts and techniques needed to get started with Elasticsearch.

By the end of this course, you will have a comprehensive understanding of Elasticsearch and be able to utilize its features for searching, analyzing, and visualizing data. Specifically, you will:

  • Learn about Elasticsearch’s core data structures, such as an inverted index, and how to index and query data effectively.

  • Gain familiarity with Elasticsearch’s powerful search capabilities, including full-text search, faceted search, and fuzzy search.

  • Gain knowledge of Elasticsearch’s aggregation and analytics features, including how to extract insights from large datasets.

  • Learn about text analysis and how Elasticsearch uses analyzers to achieve accurate search results.

  • Develop an in-depth knowledge of how Elasticsearch indexes and performs search operations.

Upon completing this course, you will have gained a solid foundation in Elasticsearch and be able to utilize it confidently in your projects.