Must Learn BigQuery Analytic Functions in 2023 Made Easy

Manisha Jena • Last Modified: February 20th, 2023

BigQuery Analytic Functions_FI

Venturing into Data Science and deciding on a tool to use to solve a given problem can be challenging at times especially when you have a wide array of choices. In this age of data transformation where organizations are constantly seeking out ways to improve the day to day handling of data being produced and looking for methods to minimize the cost of having these operations, it has become imperative to handle such data transformations in the Cloud as it is a lot easier to manage and is also cost-efficient.

Data Warehousing architectures have rapidly changed over the years and most of the notable service providers are now Cloud-based. Therefore, companies are increasingly on the move to align with such offerings on the Cloud as it provides them with a lower upfront cost, enhances scalability, and performance as opposed to traditional On-premise Data Warehousing systems. Google BigQuery is among one of the well-known and widely accepted Cloud-based Data Warehouse Applications.

In this article, you will gain information about Google BigQuery Analytic Functions. You will also gain a holistic understanding of Google BigQuery, its key features, SQL, Subqueries, and the different types of Google BigQuery Analytic Functions. Read along to find out in-depth information about undergoing Google BigQuery Analytic Functions.

Table of Contents

What is Google BigQuery?

BigQuery Analytic Functions - Google BigQuery
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Google BigQuery is a Cloud-based Data Warehouse that provides a Big Data Analytic Web Service for processing petabytes of data. It is intended for analyzing data on a large scale. It consists of two distinct components: Storage and Query Processing. It employs the Dremel Query Engine to process queries and is built on the Colossus File System for storage. These two components are decoupled and can be scaled independently and on-demand.

Google BigQuery is fully managed by Cloud service providers. We don’t need to deploy any resources, such as discs or virtual machines. It is designed to process read-only data. Dremel and Google BigQuery use Columnar Storage for quick data scanning, as well as a tree architecture for executing queries using ANSI SQL and aggregating results across massive computer clusters. Furthermore, owing to its short deployment cycle and on-demand pricing, Google BigQuery is serverless and designed to be extremely scalable.

For further information about Google Bigquery, follow the Official Documentation.

Key Features of Google BigQuery

BigQuery Analytic Functions - Key Features
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Some of the key features of Google BigQuery are as follows:

1) Scalable Architecture

BigQuery has a scalable architecture and offers a petabyte scalable system that users can scale up and down as per load.

2) Faster Processing

Being a scalable architecture, BigQuery executes petabytes of data within the stipulated time and is more rapid than many conventional systems. BigQuery allows users to run analysis over millions of rows without worrying about scalability.

3) Fully-Managed

BigQuery is a product of Google Cloud Platform, and thus it offers fully managed and serverless systems.

4) Security

BigQuery has the utmost security level that protects the data at rest and in flight. 

5) Real-time Data Ingestion

BigQuery can perform real-time data analysis, thereby making it famous across all the IoT and Transaction platforms.

6) Fault Tolerance

BigQuery offers replication that replicates data across multiple zones or regions. It ensures consistent data availability when the region/zones go down.

7) Pricing Models

The Google BigQuery platform is available in both on-demand and flat-rate subscription models. Although data storage and querying will be charged, exporting, loading, and copying data is free. It has separated computational resources from storage resources. You are only charged when you run queries. The quantity of data processed during searches is billed.

What is SQL?

BigQuery Subquery -SQL
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SQL stands for Structured Query Language and executes queries against the database for data analytics. According to ANSI (American National Standards Institute), it is the standard language for relational database management systems. 

With the help of SQL, you can:

  • Create and Delete Database
  • Create, Delete and Update Table
  • Load/UnLoad data into Tables
  • Set and Manage permissions on tables, procedures, and views
  • And many more.

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What are Google BigQuery Analytic Functions?

A BigQuery Analytic Function calculates values over a set of rows and delivers a single result for each row. This is in contrast to an aggregate function, which provides a single result for a collection of data.

An OVER clause in a BigQuery Analytic Function defines a window of rows around the row being analysed. The analytic function result is computed for each row using the specified window of rows as input, possibly doing aggregation.

You can compute moving averages, rank items, calculate cumulative sums, and perform other analyses using BigQuery analytic functions.

1) Syntax:

analytic_function_name ( [ argument_list ] ) OVER over_clause
  { named_window | ( [ window_specification ] ) }
  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC }  ] [, ...] ]
  [ window_frame_clause ]
  { rows_range } { frame_start | frame_between }
  { ROWS | RANGE }

A) Description

The clauses used in the syntax are as follows:

  • analytic_function_name: The name of the function that performs analytic operations. The numerical function RANK(), for example, might be used here.
  • argument_list: Arguments that are unique to the analytic function. Some functions have them, whereas others do not.
  • OVER: A keyword that must appear before the OVER clause in the BigQuery Analytic function syntax.
  • over_clause: Refers to a window that specifies a set of rows in a table for which a BigQuery Analytic function will be applied.
  • window_specification: Defines the window’s specifications.
  • window_frame_clause: Defines the window frame for the window.
  • rows_range: Defines a window frame’s physical rows or logical range.

B) Notes

In the query, a BigQuery Analytic function can occur as a scalar expression operand in two places:

  • The SELECT list:  If the BigQuery Analytic function appears in the SELECT list, neither its argument list nor its OVER clause can refer to aliases created in the same SELECT list.
  • The clause ORDER BY:  If the BigQuery Analytic function is used in the query’s ORDER BY clause, its parameter list can refer to SELECT list aliases.

A BigQuery Analytic function cannot, even indirectly through an alias, refer to another BigQuery Analytic function in its argument list or OVER clause.

After aggregation, a BigQuery Analytic function is evaluated. The GROUP BY clause and non-analytic aggregate functions, for example, are evaluated first. Aggregate functions can be used as input operands to BigQUery Analytic functions since they are evaluated before BigQuery Analytic functions.

2) Returns

A single result for each row in the input.

3) Defining the OVER clause

analytic_function_name ( [ argument_list ] ) OVER over_clause

  { named_window | ( [ window_specification ] ) }

A) Description

The OVER clause refers to a window that defines a set of rows in a table for which a BigQuery Analytic function will be applied. You may either provide a named window declared in your query or describe the specifications for a new window.

B) Notes

If neither a named window nor a window specification is specified, the window includes all input rows for each row.

4) Defining the Window Specification

  [ named_window ]
  [ PARTITION BY partition_expression [, ...] ]
  [ ORDER BY expression [ { ASC | DESC } ] [, ...] ]
  [ window_frame_clause ]

A) Description

The window_specification clause defines the specifications for the window.

  • named_window: The name of an existing WINDOW that was defined by WINDOW clause.
  • PARTITION BY: It divides the input rows into different partitions for which the analytic function is evaluated independently.
    • The PARTITION BY clause allows for multiple partition expressions.
    • There can be no floating-point types, non-groupable types, constants, or analytic functions in an expression.
    • If this optional clause is not included, all rows in the input table are part of a single partition.
  • ORDER BY: It specifies how rows inside a partition are arranged. In most circumstances, this clause is optional, however it is essential in some cases for navigation functions.
  • window_frame_clause: It defines the window frame within the current partition for aggregate analytic functions. What is included in the window is determined by the window frame. If this clause is used, ORDER BY is required except for fully unbounded windows.

B) Notes

If neither the ORDER BY nor the window frame clauses are present, the window frame contains all rows in that partition.

If the ORDER BY clause is present but the window frame clause is absent, the following window frame clause is applied by default for aggregate analytic functions:


For example, the following queries are equivalent:

  OVER (ORDER BY year)
FROM Library
  OVER (ORDER BY year)
FROM Library

Types of Google BigQuery Analytic functions

The different types of Google BigQuery Analytic functions are as follows:

1) Analytic Aggregate Functions

An Aggregate Function is a function that calculates a set of values. The majority of aggregate functions may be employed in analytic functions. These aggregate functions are called Analytic Aggregate functions.

The OVER clause is attached to the aggregate function call when using aggregate analytic functions; otherwise, the function call syntax stays intact. These analytic functions, like their aggregate function counterparts, conduct aggregations, but only across the applicable window frame for each row. Aggregate functions take as input all of the values in the window and output a single value.

The Analytic Aggregate Functions are:

  • MIN() (or MAX()):This functions returns the minimum (or maximum) of input values as output.
  • AVG() (or SUM()): This function returns the average (or sum) of input values as output.
  • COUNT(): This function returns the number of rows in the input as output.

2) Analytic Navigation Functions

In general, Navigation Functions compute some value_expression and assign the value to a different row in the window frame than the current row. The syntax of the OVER clause differs depending on the navigation function.

The Analytic Navigation functions are as follows:

  • FIRST_VALUE() (or LAST_VALUE()): This function returns the first (or last) value in the input as output.
  • LEAD() (and LAG()): This function returns the value on a subsequent (or preceding) row as output.
  • NTH_VALUE: This function returns the Nth value from an ordered collection of values from an analytic window. If the number of rows in the data source window is fewer than N, the NTH_VALUE function will return NULL. The NTH_VALUE function returns an error if the input n is NULL.

Requirements for the OVER clause:

  • PARTITION BY: Optional
    • PERCENTILE_CONT and PERCENTILE_DISC are not supported.
  • window_frame_clause:
    • PERCENTILE_CONT, PERCENTILE_DISC, LEAD, and LAG are not permitted.
    • Optional for FIRST_VALUE, LAST_VALUE, and NTH_VALUE.

The return data type for all navigation functions is the same as value_expression.

3) Analytic Numbering Functions

Based on the ordering, Numbering Functions assign integer values to each row.

  • ROW_NUMBER(): This function returns the order in which the rows in the input appear (starting with 1).
  • RANK(): All rows with the same value in the ordering column receive the same rank value, and the next row gets a rank value that increments by the number of rows with the previous rank value.
  • DENSE RANK(): This function returns the rank of each row in a result set partition, with no gaps in the ranking values. The rank of a specific row is one plus the number of distinct rank values preceding that row.
WITH Numbers AS
 (SELECT 1 as x
  DENSE_RANK() OVER (ORDER BY x ASC) AS dense_rank,
FROM Numbers
| x          | rank       | dense_rank | row_num    |
| 1          | 1          | 1          | 1          |
| 2          | 2          | 2          | 2          |
| 2          | 2          | 2          | 3          |
| 5          | 4          | 3          | 4          |
| 8          | 5          | 4          | 5          |
| 10         | 6          | 5          | 6          |
| 10         | 6          | 5          | 7          |
  • RANK(): For x=5, rank is 4, since this function increments by the number of peers in the previous window ordering group.
  • DENSE_RANK(): For x=5, dense_rank is 3, since this function always increments by 1, never skipping a value.
  • ROW_NUMBER(): For x=5, row_num is 4.


In this article, you have learned about BigQuery Analytic Functions. This article also provided information on Google BigQuery, its key features, SQL, Google BigQuery Analytic Functions, and the different types of Google BigQuery Analytic Functions in detail. For further information on BigQuery JSON Extract, BigQuery Create View Command, BigQuery Partition Tables, you can visit the former links.

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