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

Which analyzer is best suited for exactly matching the text without any tokenization or normalization?

Which analyzer is best suited for exactly matching the text without any tokenization or normalization?

Which analyzer is best suited for the autocomplete or suggest-as-you-type functionalities?


Which analyzer is best suited for the autocomplete or suggest-as-you-type functionalities?

Which analyzer is best suited for searching and indexing log data, where each log line should be treated as a single token?

Which analyzer is best suited for searching and indexing log data, where each log line should be treated as a single token?

Which Elasticsearch analyzer component can be used to preprocess text data by removing HTML tags or other unwanted characters?


Which Elasticsearch analyzer component can be used to preprocess text data by removing HTML tags or other unwanted characters?

Which custom token filter can be used to remove common words that are not relevant to search queries?


Which custom token filter can be used to remove common words that are not relevant to search queries?

Which custom token filter can be used to replace common spelling variations with a standard form of a word?


Which custom token filter can be used to replace common spelling variations with a standard form of a word?

Which tokenizer would be most appropriate for indexing and searching a hierarchical organizational structure, where each level of the hierarchy should be treated as a separate token?


Which tokenizer would be most appropriate for indexing and searching a hierarchical organizational structure, where each level of the hierarchy should be treated as a separate token?

It provides a visual representation of the tokenization process for debugging purposes.


It helps optimize the search performance by reducing the number of tokens in the index.


It enables the generation of alternate or additional tokens during indexing or analysis.


It converts the input text into a single token for exact matching.

What is the purpose of the token graph in Elasticsearch?



What is the purpose of the token graph in Elasticsearch?

Introduction to Elasticsearch

Getting started on Elasticsearch

Text Analysis

Search on ElasticSearch

Aggregation

Conclusion

Integrate Elasticsearch in the Ruby on Rails Application

Quiz: Text Analysis