In the realm of Data Management, a slew of new platforms and applications have emerged in recent years. Today, most businesses are turning to Cloud-based solutions to gather, store, and interact with enormous amounts of data. One of the popular Cloud-based Data Warehouses is Google BigQuery which comes with many features such as BigQuery SQL, BigQuery ML, integrations, Cloud Functions, etc.
Google BigQuery is one of the popular Cloud-based Data Warehouses that offers its users great scalability, simplicity, and abstraction. Unlike its competitors, it is serverless and hence delivers services dynamically, which means that the user is not required to perform any provisioning or hardware management.
Querying commands in Google BigQuery is based on Structured Query Language (SQL), which is a domain-specific querying language designed specifically for Database Management Systems and is especially helpful when dealing with structured data.
This article will give you a comprehensive guide to Google BigQuery SQL. You will get to know about Google BigQuery and its key features. You will also explore the Google BigQuery SQL syntax, functions, and key benefits of utilizing it in the further sections. Let’s get started.
Table of Contents
What is Google BigQuery?
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Google BigQuery is a fully managed Data Warehouse that comes with built-in functionalities like Machine Learning, Geospatial Analysis, and Business Intelligence to help you manage and analyze your data. It provides a serverless architecture that enables you to utilize SQL queries to solve your organization’s biggest needs while requiring no infrastructure management. Moreover, the scalable and distributed analytical engine in Google BigQuery allows you to query terabytes of data in seconds and petabytes of data in minutes.
Google BigQuery also provides more flexibility by separating the computing engine from the storage space. This feature enables Google BigQuery to store, analyze, and assess data regardless of where it is stored. Additionally, Google BigQuery also includes sophisticated tools for analyzing and understanding data, such as Google BigQuery ML (Machine Learning) and BI (Business Intelligence) Engine.
Key Features of Google BigQuery
Google BigQuery has gained tremendous popularity in the market. Here are some of the key features of Google BigQuery:
- Scalability: Google BigQuery leverages massively parallel processing and a highly scalable secure storage engine to deliver scalability and consistent performance to customers.
- Manageability: By offering serverless execution, Google BigQuery takes care of difficult maintenance and setup operations like Server/VM Administration, Server/VM Sizing, Memory Management, and many more.
- Built-in AI (Artificial Intelligence) and ML (Machine Learning): Its auto ML feature aids in the development of high accuracy models and enable Predictive Analysis.
- Storage: To store massive amounts of data, Google BigQuery utilizes a Column-based Architecture. Columnar storage provides numerous advantages over traditional row-based storage, including improved storage usage and the ability to scan data quicker.
To know more about Google BigQuery, visit this link.
What is Google BigQuery SQL?
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Google BigQuery SQL (Structured Query Language) is a domain-specific querying language for managing data in RDBMS (Relational Database Management System) or Data Warehouses like Google BigQuery. Donald D.Chemberlin and Raymond F.Boyce developed it, and its stable version was released in December 2016.
Relational Algebra and Tuple Relational Calculus were the foundations of SQL. It was also one of the first commercial languages to employ the relational model proposed by Edgar F. Codd. Today, SQL is not only being utilized by Database Administrators, but also by developers creating Data Integration scripts, and Data Analysts setting up and running analytical queries. There are four sublanguages in SQL:
- Data Definition Language (DDL): The database structure or schema is defined using Data Definition Language. These statements specify the database schema implementation details that are normally hidden from users.
- Data Query Language (DQL): DQL statements are used to query data stored in schema objects. The DQL Command’s purpose is to obtain a schema relation based on the query that is provided to it.
- Data Manipulation Language (DML): DML statements are used to manage data that is contained within schema objects.
- Data Control Language (DCL): DCL statements are used to control access to data contained in a database (Authorization).
Key Features of Google BigQuery SQL
SQL has gained significant popularity in the market. Some of the key features of SQL include:
- High Performance: SQL provides high-performance programming capabilities for database systems that are highly transactional, have a high workload, and are frequently used.
- High Availability: SQL works with databases such as MS SQL Server, MS Access, MySQL, Oracle Database, SAP Adaptive Server, and many others. SQL is supported by all of these Relational Database Management Systems, and creating a procedural programming extension is very simple.
- Scalability and Flexibility: New tables can be easily added, and previously created or unused tables may be dropped or deleted from a database. Thus, providing both Scalability and Flexibility.
- Ease of Management: Almost every Relational Database Management System uses SQL. The SQL commands “Select,” “Create,” “Insert,” “Drop,” “Update,” and “Delete” are standard and common SQL statements that allow us to rapidly and efficiently manage large amounts of data from a database.
To know more about Google BigQuery SQL, visit this link.
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Dialects of Google BigQuery SQL
When Google BigQuery was originally released, all queries were written in “BigQuery SQL”, a non-standard SQL dialect. However, “BigQuery SQL” was renamed Legacy SQL after a new version of Google BigQuery was launched in 2016 that included support for Standard SQL. Today, Google BigQuery supports both dialects of SQL:
1) Google BigQuery Legacy SQL
Before Google BigQuery v2.0, the Data Warehouse used a non-standard SQL dialect called Legacy SQL to run queries. However, this SQL version is currently optional and can be activated by adding the “legacy_sql: true” parameter in your query.
For example, the below query depicts the Legacy SQL in Google BigQuery:
require "google/cloud/bigquery"
bigquery = Google::Cloud::Bigquery.new
sql = "SELECT word_id FROM [bigquery-public-data:samples.shakespeare]"
data = bigquery.query sql, legacy_sql: true
2) Google BigQuery Standard SQL
Google BigQuery Standard SQL is the most frequently used SQL dialect for querying data stored in Google BigQuery. It follows the SQL 2011 standard and also supports nested queries and repeated data. It offers a few additional data types and has a strict grammar syntax.
For example, the below query depicts the Standard SQL in Google BigQuery:
require "google/cloud/bigquery"
bigquery = Google::Cloud::Bigquery.new
sql = "SELECT word_id FROM [bigquery-public-data:samples.shakespeare]"
data = bigquery.query sql
Google BigQuery SQL Syntax
Google BigQuery SQL follows a set of rules and standards called query syntax. The syntax for SQL queries in Google BigQuery is listed below.
query_statement:
query_expr
query_expr:
[ WITH cte[, ...] ]
{ select | ( query_expr ) | set_operation }
[ ORDER BY expression [{ ASC | DESC }] [, ...] ]
[ LIMIT count [ OFFSET skip_rows ] ]
select:
SELECT [ AS { STRUCT | VALUE } ] [{ ALL | DISTINCT }]
{ [ expression. ]* [ EXCEPT ( column_name [, ...] ) ]
[ REPLACE ( expression [ AS ] column_name [, ...] ) ]
| expression [ [ AS ] alias ] } [, ...]
[ FROM from_clause[, ...] ]
[ WHERE bool_expression ]
[ GROUP BY { expression [, ...] | ROLLUP ( expression [, ...] ) } ]
[ HAVING bool_expression ]
[ QUALIFY bool_expression ]
[ WINDOW window_clause ]
The following are the notation rules for the above mentioned Google BigQuery SQL syntax:
- Optional clauses are denoted by square brackets “[ ]”.
- Literal parentheses are denoted by the “( )”.
- A logical OR is indicated by the vertical bar “|”.
- A set of options is denoted by “{ }”.
- In a comma-separated list “[,… ]”, a comma followed by an ellipsis within square brackets indicates that the preceding item can be repeated.
Also, note that you effectively create the clauses while creating a query expression. Clauses are the commands that can be used to create a query expression. SELECT, FROM, WHERE, and GROUP BY are some of the commonly used SQL clauses. The clauses must be in the correct order, as shown in the image above.
Let’s take a look at some of the SQL syntax for widely used clauses in Google BigQuery.
1) Google BigQuery SQL Syntax: SELECT Clause
The SELECT clause is most widely used in SQL. It is used to fetch data from a database. You can retrieve the entire table or a subset of it based on some rules.
Basic Syntax:
SELECT column1, column2… columnN
FROM table_name;
2) Google BigQuery SQL Syntax: FROM Clause
The FROM clause specifies the source from which you want to extract information. This might be a single table or a group of tables.
Basic Syntax:
SELECT * FROM table_name;
3) Google BigQuery SQL Syntax: WHERE Clause
Filtering the records in a table is done using the WHERE clause. It is used to retrieve only the records that meet a specific set of criteria.
Basic Syntax:
SELECT column1, column2, … columnN
FROM table_name
WHERE condition;
4) Google BigQuery SQL Syntax: GROUP BY Clause
The GROUP BY clause is used to group identical data into groups using specified methods. Few things to be noted while using the GROUP BY clause:
- The GROUP BY clause is used only while using the SELECT statement.
- The GROUP BY clause comes after the WHERE clause in the query.
- If an ORDER BY clause is used in the query, GROUP BY is placed before them.
Basic Syntax:
SELECT column1, function_name(column2)
FROM table_name
WHERE condition
GROUP BY column1, column2
ORDER BY column1, column2;
5) Google BigQuery SQL Syntax: HAVING Clause
The HAVING clause is identical to the WHERE clause, however, it focuses on the results generated by the GROUP BY clause.
Basic Syntax:
SELECT column1, function_name(column2)
FROM table_name
WHERE condition
GROUP BY column1, column2
HAVING condition
6) Google BigQuery SQL Syntax: ORDER BY Clause
The ORDER BY clause sorts the obtained data in ascending or descending order based on one or more columns. The data is sorted in ascending order by default.
Basic Syntax:
SELECT * FROM table_name
ORDER BY column_name ASC|DESC;
Google BigQuery SQL Functions
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Google BigQuery Standard SQL provides a wide range of built-in functions, such as Logical and Statistical aggregate functions, Mathematical functions, String functions, Date functions, and many more. User-defined functions (UDFs) are also supported in Google BigQuery and may be utilized to expand the sorts of analysis that can be performed. Working with Aggregation, Window, String, and Date are the most often utilized functions in Google BigQuery.
1) Aggregation Functions
Aggregation functions are among the most often used SQL functions. An aggregate function combines all of a group’s rows into a single value. Some of the commonly used aggregation functions include:
| Aggregate Functions | Description |
| COUNT() | Counts the number of rows in a table or view. |
| SUM() | Computes the sum of all values. |
| AVG() | Computes the average of a set of values. |
| MIN() | Returns the minimum value. |
| MAX() | Returns the maximum value. |
Aggregate Functions2) Window (Analytical) Functions
Window functions evaluate values for a certain window, or a selection of rows that you’re interested in, rather than the full table. You must use an OVER() clause to specify the Window. Some of the commonly used Window functions include:
| Window Functions | Description |
| RANK() | It is used to assign each record a unique rank based on a set of criteria. |
| ROW_NUMBER() | Each record is given a unique row number by this function. |
| NTILE() | It aids in determining which percentile a particular row belongs to. |
| LAG() | It enables you to get data from the preceding row in the same result set without having to use SQL joins. |
| LEAD() | It enables you to get data from the next row in the same result set without having to use SQL joins. |
Window Functions3) String Functions
For handling text fields, String functions are quite helpful. When you need to alter text, format text in a line, or glue the values of columns, they are helpful. Some of the commonly used string functions include:
| String Functions | Description |
| CONCAT() | It helps to join two words or strings together. |
| CONCAT_WS() | It is used to join two words or strings together by using a symbol as a concatenating symbol. |
| FORMAT() | It is used to display the number in a specified format. |
| RTRIM() | It is used to separate the given substring from the rest of the string. |
| SUBSTR() | It is used to extract a substring from a string at a certain position. |
| REVERSE() | It is used for reversing a string. |
String Functions4) Date Functions
When you need to deal with dates in your dataset, date functions prove useful. Some of the frequently used Date functions include:
| Date Functions | Description |
| DATE() | It is responsible for creating a DATE field from integer values for the year, month, and day. |
| PARSE_DATE() | It creates a DATE object from a string representation of a date. |
| DATE_DIFF() | It counts the number of days, weeks, months, or years that have elapsed between two dates. |
| CURRENT_DATE() | The current date in the given or default timezone is returned. |
| FORMAT_DATE() | It formats the DATE field to the given format string. |
Date FunctionsKey Benefits of Google BigQuery SQL
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Google BigQuery SQL has dominated the entire market. Some of the key benefits include:
- BigQuery SQL assists organizations in extracting the most information from data through deep segmentation, marketing KPI research, and the discovery of unfair contractors in CPA (Cost per Action) networks.
- You can do advanced analysis with BigQuery SQL, such as user segmentation and determining your site’s best-performing audiences.
- BigQuery SQL can be used in ROPO (Research Online, Purchase Offline) analysis. It aids in determining the impact of online campaigns on offline sales.
- Advanced SQL is the most powerful tool an analyst can learn to help the business make better decisions.
Conclusion
This article gave you a brief introduction to Google BigQuery SQL along with its key features. It also provided in-depth knowledge about Google BigQuery SQL syntax, functions, and their key benefits. You may now create your queries and examine your data to improve your analytics capabilities and make better decisions.
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Ayush is a Software Engineer with a keen interest in data analysis and writing technical content. He has authored numerous articles covering a wide array of subjects in data integration and infrastructure.