Google BigQuery MERGE SQL Command: 3 Critical Aspects


BigQuery Merge: Featured Image

You may be surprised to know that 57% of your customers are willing to provide their personal data to receive discounts or personalized offers? Your audience’s willingness to share their private information with you can help you convert Leads into Sales if you leverage it appropriately. In addition, you can take advantage of tools like Google’s BigQuery to make the most of your organization’s data. 

Currently, Google BigQuery is becoming a buzzword among Marketers, Analysts, and Consumer Insights Professionals on a large scale. However, many organizations are not aware of how they can leverage Google BigQuery to keep their Data Engine moving forward. They can avoid the need of hiring a Database Administrator by analyzing their data to extract meaningful insights using simple SQL commands.

This article will introduce you to Google BigQuery and elaborate on its key features. It will also brief you about the Google BigQuery MERGE Command and explain it using 3 examples. Furthermore, the article will list down the best practices of using the Google BigQuery MERGE command. Read along to learn the importance and usage of the MERGE Command for Google BigQuery!

Table of Contents

Introduction to Google Bigquery

BigQuery Merge: Google BigQuery Logo
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Google BigQuery is a serverless Data Warehouse that enables you to manage and analyze your data using its built-in features like Geospatial Analysis, Machine Learning Tools, BI Tools (Business Intelligence), etc.

The structure of Google BigQuery makes it possible to use Structured Query Language (SQL) to seek answers to questions relating to your organization’s data management without thinking about infrastructure management. It also uses a scalable and Distributed Analysis Engine that queries tetra byte data in seconds and petabyte data in minutes.

Google BigQuery separates the computing engine that analyses data from your storage choices to ensure maximal flexibility. It uses interfaces such as the Google Cloud Console interface and the command-line tool to execute its task.

Key Features of Google BigQuery

Google BigQuery is a widely-used Data Warehouse and its popularity is due to its rich feature-set. Some of the key features of Google BigQuery include:

  • Serverless Computing: The serverless model gives you access to resources at the right time. As a result of Google BigQuery, you can focus on your data analysis and analysis rather than on the operation and sizing of computing resources.
  • Petabyte Scaling: Google BigQuery’s Petabyte scale and ease of use make it ideal for large data sets. You won’t have to buy more storage or compute capacity with Google BigQuery since it can scale seamlessly and analyze petabytes of data without sacrificing performance. 
  • Real-time Analytics: Using Google BigQuery’s high-speed streaming API, real-time analytics can be performed with powerful results. In Google BigQuery, your latest business data is made instantly available for analysis so you can analyze what’s happening right now in real-time.
  • Flexibility in Pricing: Depending on your needs, you can choose from various pricing options in Google BigQuery. The on-demand pricing approach allows you to pay only for the storage and computing power you use. Users and enterprises who utilize flat-rate pricing can choose a stable monthly price based on usage volume.
  • High Availability: In cases of extreme failure, your data can be accessed even when it has been replicated in multiple locations. In addition, Google BigQuery automatically and transparently provides high availability and durable storage at no charge and without additional configuration.
  • Security: Google BigQuery allows you to have complete control over who has access to your store data. With Google BigQuery, you can easily manage identity and access with Cloud Identity and Access Management, and your data is always encrypted at rest and during transmission.
  • Support Standard SQL: The Google BigQuery SQL dialect is compliant with ANSI:2011, so no code rewriting is required, and you can use advanced SQL features. In addition, you can connect your current applications to Google BigQuery’s powerful engine with free ODBC and JDBC drivers. This is very useful for less knowledgeable people like me who hate learning new things every day.
  • Foundations for AI & BI: Machine learning and artificial intelligence are greatly enhanced thanks to Google BigQuery’s flexibility and power. Additionally, you can train powerful models on structured data using Cloud ML Engine and TensorFlow integrations with Google BigQuery ML and Cloud ML Engine. Also, Google BigQuery’s ability to analyze and transform data can help you prepare your data for machine learning.

Also, Google BigQuery is a core part of modern enterprise BI solutions, allowing them to integrate, transform, analyze, visualize, and report on data with tools from Google and our technology partners.

To learn more about Google BigQuery, visit here.

Introduction to Google BigQuery MERGE Command

MERGE is a statement that is generally used in relational databases. Google BigQuery MERGE Command is one of the Data Manipulation Language (DML) statements. It is often used to perform three main functions atomically in one single statement. These functions are UPDATE, INSERT, and DELETE.

  • UPDATE or DELETE clause can be used when two or more data match.
  • INSERT clause can be used when two or more data are different and do not match.
  • The UPDATE or DELETE clause can also be used when the given data does not match the source.

This means that the Google BigQuery MERGE Command enables you to merge Google BigQuery data by updating, inserting, and deleting data from your Google BigQuery tables.

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Examples of Google BigQuery MERGE Command

Now since you know, what Google BigQuery MERGE Command is, you can dive into its real-time examples. The following 3 examples of the Google BigQuery MERGE Command will help you in understanding its syntax and use cases:

Google BigQuery MERGE Command Example 1

New items are added to the DetailedInventory table from the Inventory table in the following example. If an item has low inventory, the supply_constrained value is set to true, and comments are added to the item.

Syntax of the MERGE Command

BigQuery Merge: Syntax of Example 1
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Here are the tables you need before running the query:

BigQuery Merge: Table of Example 1
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BigQuery Merge: Table of Example 1
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As a result of the above query, you will get the following output:

BigQuery Merge: Table of Example 1
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Google BigQuery MERGE Command Example 2

The query increments the quantity field of an existing item in inventory. If an item does not exist, a new row is inserted. By merging the NewArrivals and Inventory tables in the query, items from the NewArrivals table are included in the Inventory table.

Syntax of the MERGE Command

BigQuery Merge: Syntax of Example 2
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The following are the tables before running the query:

BigQuery Merge: Table of Example 2
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BigQuery Merge: Table of Example 2
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After running the query, here is the Inventory table:

BigQuery Merge: Table of Example 2
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Google BigQuery MERGE Command Example 3

By using the following query, the NewArrivals table is updated by 20 new products from warehouse #1. Except for those imported from warehouse #2, all other products are deleted.

Syntax of the MERGE Command

BigQuery Merge: Syntax of Example 3
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Before running the query, here is the NewArrivals table:

BigQuery Merge: Table of Example 3
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After running the query, here is the NewArrivals table:

BigQuery Merge: Table of Example 3
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Best Practices for Using Google BigQuery MERGE Command

When you are using the MERGE Query, it is essential to take note of the following guidelines:

  • Any column of the record data type must not be marked for update or upsert assertion when a Google BigQuery connection is configured to use a simple connection mode, and CSV is selected as the staging format. 
  • The target table in Google BigQuery must not contain repeated columns when a Google BigQuery connection is configured to use a simple connection mode.
  • The target table in Google BigQuery must not contain repeated columns as the key field when a Google BigQuery connection is configured to use a hybrid connection mode.
  • When an update or upsert operation is performed on a column of recorded data type, there must be no required fields in the records, especially if the column is nullable.
  • The columns must not contain NULL values when an upsert operation is performed on a Google BigQuery target table. This ensures that the mapping does not fail.


This article introduced you to Google BigQuery and explained its important features. It further discussed the Google BigQuery MERGE Command and provided 3 examples of the same. These examples presented the syntax and use cases of this popular Google BigQuery command. The article also listed down the best practices that you must follow to obtain the best results while using the Google Biguery MERGE Command.

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Samuel Salimon
Technical Content Writer, Hevo Data

Samuel is a versatile writer specializing in the data industry. With over seven years of experience, he excels in data science, data integration, and data analysis, crafting engaging content on these topics. He is also adept at WordPress development. Samuel holds a Bachelor's degree in Computer Science from Lagos State University.

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