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

# Challenge 

You are working with a dataset of movie information stored in Elasticsearch. The dataset includes several attributes for each movie, such as `title`, `lifetime_gross`, `year`, `studio`, `rating`, `runtime`, `budget`, `releaseDate`, `voteAvg`, and `voteCount`. Your task is to perform different aggregation queries in Elasticsearch.

The `movies` index comprises several fields with their respective data types and purposes, including:

- `title`: This `text` field stores the title of the movie. It allows full-text search capabilities, enabling users to efficiently search for movies based on their titles.

- `lifetime_gross`: This field stores the total amount of money the movie has made in its lifetime. It is represented as an `integer` type, allowing for numeric calculations and range-based searches, such as finding movies with specific earnings.

- `year`: The `year` field represents the year the movie was released. It is stored as an `integer` data type.

- `studio`: This field contains the name of the studio that produced the movie. It is stored as a `text` type, enabling full-text search capabilities.

- `rating`: The `rating` field stores the rating of the movie (PG-13, R, etc.). It is stored as a `text` type.

- `runtime`: This field represents the length of the movie in minutes. It is stored as an `integer`.

- `budget`: This field contains the budget of the movie in USD. It is stored as an `integer`.

- `releaseDate`: This field indicates the date the movie was released. It is stored as a `date` type.

- `voteAvg`: This field represents the average rating of the movie from the users. It is stored as a floating-point number.

- `voteCount`: This field stores the total number of votes the movie has received from users. It is stored as an `integer` type.






Test your aggregation writing skills in Elasticsearch.


Introduction to Elasticsearch

Getting started on Elasticsearch

Text Analysis

Search on ElasticSearch

Aggregation

Conclusion

Integrate Elasticsearch in the Ruby on Rails Application

Challenge: Aggregations

Challenge