Strings are one of the most commonly used data types, representing sequences of characters. Whether it’s textual data or encoded values, strings are ubiquitous. Google BigQuery offers a myriad of functions to manipulate, analyze, and transform these strings, granting a high degree of flexibility and precision in your queries. String functions in Google BigQuery are not only powerful but also versatile, enabling a broad spectrum of data manipulation techniques.
Use Cases of REGEXP_CONTAINS, REGEXP_EXTRACT, and REGEXP_REPLACE
Regular expressions (regex) are powerful tools for pattern matching and string manipulation. BigQuery integrates seamlessly with regex functions, enabling intricate and detailed string operations.
This function checks if a string contains a specific pattern.
Use Case: Identify pages with names containing the word “Home”.
SELECT page_name FROM freshers_in_most_visited_pages WHERE REGEXP_CONTAINS(page_name, r'Home');
This extracts portions of a string based on a regex pattern.
Use Case: Extract the first word from the page_name.
This replaces portions of a string based on a regex pattern.
Use Case: Replace all occurrences of “Home” with “Main”.
SELECT page_name, REGEXP_REPLACE(page_name, r'Home', 'Main') AS modified_name FROM freshers_in_most_visited_pages;
Techniques for Splitting and Concatenating Strings
Strings often contain multiple values or are combined from various sources. The ability to split or concatenate them efficiently is essential.
Using the SPLIT function, you can break apart a string.
Use Case: Split page_name by underscores.
SELECT page_name, SPLIT(page_name, '_') AS split_array FROM freshers_in_most_visited_pages;
Combine multiple strings using the CONCAT or STRING_AGG functions.
Use Case: Combine page name with a prefix.
SELECT page_name, CONCAT('Page: ', page_name) AS full_name FROM freshers_in_most_visited_pages;
BigQuery import urls to refer