Google's search algorithm, often shrouded in mystery, is renowned for its accuracy in delivering relevant results, yet developers can implement similar search capabilities through full-text search engines like Elastic. Traditional wildcard-based SQL queries, while useful, often fall short in terms of relevancy, especially when dealing with unstructured content, prompting the use of more advanced techniques like inverted indexing. Elastic, a dedicated search engine, offers enhanced search functionalities by indexing words and their positions within documents, enabling efficient and relevant search results through its RESTful API. The process involves creating an index, mapping document properties, and indexing content, which allows for powerful search capabilities that consider the relevance of matches in different parts of a document. This approach demonstrates a trade-off between the computational expense of indexing versus the efficiency of searching, making it suitable for applications that require frequent searches. Elastic further allows customization, such as boosting the relevance of certain matches, which developers can use to tailor search results to specific user preferences, drawing parallels to the sophisticated search capabilities of major search engines like Google.