BigQuery Columns to Rows: Using Pivot & Unpivot Operators Simplified 101


BigQuery Columns to Rows

Pivot and Unpivot are Relational Operators used to transform one table into another in order to obtain a more simplified view of the data. Conventionally, the Pivot operator converts the table’s row data into column data. The Unpivot operator does the inverse, transforming column-based data into rows. Google BigQuery offers operators like Pivot and Unpivot that can be used to transpose the BigQuery columns to rows or vice versa. This article will introduce you to the Pivot and Unpivot operators along with their syntax and example queries. Read along to learn more about transforming BigQuery columns to rows and vice versa!

Table of Contents

What is Google BigQuery?

Google BigQuery is a robust Cloud-based Data Warehouse and Analytics platform. It is a serverless platform that does not require the installation of any software or maintenance and management of large infrastructure. It is a very cost-effective solution for a growing business as it eliminates the need for large server rooms and the significant hardware investment required in the case of traditional On-Premise Databases. You can query Terabytes and Petabytes of data in a matter of just a few minutes using Google BigQuery’s Scalable and Distributed Analytics Engine.

Google BigQuery is Serverless and built to be highly Scalable. Google leverages its existing Cloud architecture to successfully manage a Serverless design, as well as various Data Models that enable users to store Dynamic Data. It also supports Machine Learning (ML) operations by allowing users to use the BigQuery ML functionality. BigQuery ML allows users to develop and train various Machine Learning Models by querying data from the desired database using built-in SQL capabilities. Later in this article, you will also learn about transforming BigQuery columns to rows and vice versa.

Key Features of Google BigQuery

Some of the key features of Google BigQuery are as follows:

  • Scalability: To provide consumers with true Scalability and consistent Performance, Google BigQuery leverages Massively Parallel Processing (MPP) and a Highly Scalable Secure Storage Engine. The entire Infrastructure with over a thousand machines is managed by a complex software stack.
  • Serverless: The Google BigQuery Serverless Model automatically distributes processing across a large number of machines running in parallel, so any organization using Google BigQuery can focus on extracting insights from data rather than configuring and maintaining the Infrastructure/Server. 
  • Storage: Google BigQuery uses a Columnar Architecture to store a mammoth scale of datasets. Column-based Storage has several advantages, including better Memory Utilization and the ability to scan data faster than typical Row-based Storage.
  • Integrations: Google BigQuery as a part of the Google Cloud Platform (GCP) supports seamless integration with all Google products and services. Google also offers a variety of Integrations with numerous third-party services, as well as the functionality to integrate with application APIs that are not natively supported by Google.

For further information on Google BigQuery, you can click here to check out their official website.

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How to use the Google BigQuery Pivot Operator?

Transforming BigQuery Rows to Columns
Image Source

At times, you might want to reformat a table result so that each unique value has its own column. This is known as a Pivot Table, and it is normally only a display function supported by BI tools. However, it can be occasionally useful to create the Pivot Table in Google BigQuery. Here’s how to make a pivot table in Google BigQuery using the Pivot Operator:

A) Syntax

FROM from_item[, ...] pivot_operator

        aggregate_function_call [as_alias][, ...]
        FOR input_column
        IN ( pivot_column [as_alias][, ...] )
    ) [AS alias]

    [AS] alias

B) Usage Parameters

The arguments/parameters associated with the Google BigQuery Pivot operator are as follows:

  • from_item represents the table or subquery on which you need to perform the Pivot operation.
  • alias represents a name used for an item in the query.

C) Rules

Following are the rules for the from_item argument passed to a Pivot operation:

  • from_item might constitute any table or result subquery.
  • It is possible that it might not generate a value table.

Following the rules for the aggregate_function_call argument:

  • You can refer to columns in a table passed to the Pivot operator as well as correlated columns, but you can’t access columns defined by the Pivot Clause.
  • If an alias is provided, a table passed to the Pivot operator can be accessed via its alias.
  • An Aggregate Function with a single argument can be used only once.
  • You can only use Aggregate Functions that ignore NULL inputs, with the exception of the COUNT Function.

Following are the rules for the input_column argument:

  • The input column is evaluated against each row in the input table.
  • If an alias is provided, the input table can be accessed via its alias.

Following are the rules for pivot_column:

  • A Pivot column must be a constant.
  • If a name is required for a named constant or query parameter, use an alias to specify it explicitly.
  • There are some cases where different Pivot columns can end up with the same default column names. For example, an input column could have a NULL value as well as the string literal “NULL”. When this happens, multiple Pivot columns with the same name are created. You can use aliases for Pivot column names to avoid this situation.

D) Conceptual Example: Transforming BigQuery Rows to Columns

The Pivot operation in Google BigQuery changes rows into columns by using Aggregation. Let’s understand the working of the Pivot operator with the help of a table containing information about Products and their Sales per Quarter. The following examples reference a table called Produce that looks like this before applying the Pivot operation:

WITH Produce AS (
  SELECT 'Win' as product, 51 as sales, 'Q1' as quarter UNION ALL
  SELECT 'Win', 23, 'Q2' UNION ALL
  SELECT 'Win', 45, 'Q3' UNION ALL
  SELECT 'Win', 3, 'Q4' UNION ALL
  SELECT 'Linux', 77, 'Q1' UNION ALL
  SELECT 'Linux', 0, 'Q2' UNION ALL
  SELECT 'Linux', 25, 'Q3' UNION ALL
  SELECT 'Linux', 2, 'Q4')

| product | sales | quarter |
| Win     | 51    | Q1      |
| Win     | 23    | Q2      |
| Win     | 45    | Q3      |
| Win     | 3      | Q4      |
| Linux   | 77    | Q1      |
| Linux   | 0      | Q2      |
| Linux   | 25    | Q3      |
| Linux   | 2      | Q4      |

After applying the Pivot operator, you can rotate the Sales and Quarter into Q1, Q2, Q3, and Q4 columns. This will make the table much more readable. The query for the same would look something like this:

  (SELECT * FROM Produce)
  PIVOT(SUM(sales) FOR quarter IN ('Q1', 'Q2', 'Q3', 'Q4'))

| product | Q1 | Q2 | Q3 | Q4 |
| Win      | 77 | 0  | 25 | 2  |
| Linux    | 51 | 23 | 45 | 3  |

How to Use the Google BigQuery Unpivot Operator?

Transforming BigQuery Columns to Rows
Image Source

The Unpivot Operator is used to transpose the BigQuery columns to rows. Here is the syntax that you can use to transform BigQuery columns to rows:

A) Syntax

FROM from_item[, ...] unpivot_operator

        { single_column_unpivot | multi_column_unpivot }
    ) [unpivot_alias]

    FOR name_column
    IN (columns_to_unpivot)

    FOR name_column
    IN (column_sets_to_unpivot)

    (values_column[, ...])

    unpivot_column [row_value_alias][, ...]

    (unpivot_column [row_value_alias][, ...])

unpivot_alias and row_value_alias:
    [AS] alias

B) Usage Parameters

The arguments/parameters associated with the Google BigQuery Unpivot operator in order to transform BigQuery columns to rows are as follows:

  • from_item represents a table or subquery on which you need to apply the Unpivot operation.
  • The argument INCLUDE NULLS adds rows with NULL values to the result.
  • EXCLUDE NULLS is the opposite of the INCLUDE NULLS argument. It does not add rows with NULL values to the result.
  • single_column_unpivot splits a column into two values columns and one name column.
  • multi_column_unpivot splits columns into several values columns and one name column.
  • unpivot_alias represents an alias for the UNPIVOT operation’s results. 
  • values_column represents a column containing the row values from columns_to_unpivot.
  • Name_column represents a column containing the column names from columns_to_unpivot.
  • values_column_set represents a set of columns containing the row values from columns_to_unpivot.

C) Conceptual Example: Transforming BigQuery Columns to Rows

To understand the working of the UNPIVOT operator and transforming BigQuery columns to rows, let us consider the output table you used to understand the Pivot operation.

UNPIVOT(sales FOR quarter IN (Q1, Q2, Q3, Q4))

| product | sales | quarter |
| Win    | 51    | Q1      |
| Win    | 23    | Q2      |
| Win    | 45    | Q3      |
| Win    | 3     | Q4      |
| Linux   | 77    | Q1      |
| Linux   | 0     | Q2      |
| Linux   | 25    | Q3      |
| Linux   | 2     | Q4      |

After applying the UNPIVOT operator, the columns Q1, Q2, Q3, and Q4 are rotated. These columns’ values now populate a new column called Sales, and their names populate a new column called Quarter. This is a single-column unpivot procedure.


This article introduced you to the steps required to transpose the BigQuery Columns to Rows. It discussed the PIVOT and UNPIVOT operators in great detail. With your Data Warehouse, Google BigQuery live and running, you’ll need to extract data from multiple platforms to carry out your analysis. However, integrating and analyzing your data from a diverse set of data sources can be challenging. BigQuery transpose and BigQuery pivot operations, such as Bigquery pivot columns to rows, can simplify this process. This is where Hevo Data comes into the picture

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Share your experience of learning about transposing BigQuery Columns to Rows. Let us know in the comments section below!

Former Research Analyst, Hevo Data

Rakesh is a Cloud Engineer with a passion for data, software architecture, and writing technical content. He has experience writing articles on various topics related to data integration and infrastructure.

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