
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

In the context of searches in Elasticsearch, which term refers to the measure of how well the search results match the user's query and intent?


In the context of searches in Elasticsearch, which term refers to the measure of how well the search results match the user’s query and intent?

John wants to search for documents that contain an exact match of the term `"apple"` in the `product_name` field. Which query should they use?


John wants to search for documents that contain an exact match of the term <code>&quot;apple&quot;</code> in the <code>product_name</code> field. Which query should they use?

Sarah wants to search for documents that match the term `"car"` in the `product_name` field, but they also want to consider synonyms like `"automobile"` and `"vehicle"`. Which query should they use?


Sarah wants to search for documents that match the term <code>&quot;car&quot;</code> in the <code>product_name</code> field, but they also want to consider synonyms like <code>&quot;automobile&quot;</code> and <code>&quot;vehicle&quot;</code>. Which query should they use?

Emma wants to search for documents that have a numeric value between 100 and 200 in the `price` field. Which query should they use?


Emma wants to search for documents that have a numeric value between 100 and 200 in the <code>price</code> field. Which query should they use?

Olivia wants to search for documents that contain the term `"apple"` in the `product_name` field and exclude documents that have the term `"red"` in the `color` field. Which query should they use?


Olivia wants to search for documents that contain the term <code>&quot;apple&quot;</code> in the <code>product_name</code> field and exclude documents that have the term <code>&quot;red&quot;</code> in the <code>color</code> field. Which query should they use?

Alex wants to search for documents that contain a term similar to `"cat"` in the `product_name` field, allowing for slight misspellings or variations. Which query should they use?


Alex wants to search for documents that contain a term similar to <code>&quot;cat&quot;</code> in the <code>product_name</code> field, allowing for slight misspellings or variations. Which query should they use?

Lisa wants to search for documents that match the phrase `"red apple"` in the `product_description` field and wants the terms to appear in the exact order. Which query should they use?


Lisa wants to search for documents that match the phrase <code>&quot;red apple&quot;</code> in the <code>product_description</code> field and wants the terms to appear in the exact order. Which query should they use?

The edit distance is a measure of the number of exact character matches between two strings.

The edit distance is a metric that calculates the similarity between two strings based on their common substrings.


The edit distance is a numerical value that indicates the dissimilarity or the number of operations needed to transform one string into another.


The edit distance is a technique that utilizes machine learning algorithms to improve the accuracy of search queries.



Which statement best describes the edit distance used in fuzzy search?



Which statement best describes the edit distance used in fuzzy search?

Which option best represents the flow of the match query in Elasticsearch?



Which option best represents the flow of the match query in Elasticsearch?

Precision measures the percentage of relevant documents retrieved out of all relevant documents in the collection.



Precision measures the percentage of retrieved documents that are relevant out of all retrieved documents.

Precision measures the recall of a search algorithm by evaluating the number of true positive results.

Precision measures the accuracy of a search algorithm by comparing the number of relevant documents to the total number of documents in the collection.

Which statement best describes precision in information retrieval?



Which statement best describes precision in information retrieval?

Recall measures the percentage of relevant documents retrieved out of all relevant documents in the collection.

Recall measures the percentage of retrieved documents that are relevant out of all retrieved documents.



Recall measures the precision of a search algorithm by evaluating the number of true positive results.



Recall measures the accuracy of a search algorithm by comparing the number of relevant documents to the total number of documents in the collection.



Which statement best describes recall in information retrieval?



Which statement best describes recall in information retrieval?

Test your query writing knowledge of Elasticsearch.


Introduction to Elasticsearch

Getting started on Elasticsearch

Text Analysis

Search on ElasticSearch

Aggregation

Conclusion

Quiz: Search on Elasticsearch