Export BigQuery Table to CSV: 3 Easy Methods

on Data Extraction, Data Warehouse, Google BigQuery, Google Cloud Platform, Python • September 9th, 2021 • Write for Hevo

The exponential rate of data generation in every modern business from various SaaS applications, Marketing Channels, etc. has compelled them to move from On-premise databases to Cloud-Based Data Warehouses. In traditional Data Warehousing Solutions, business teams often found it challenging to run large queries or apply Machine Learning to study a trend, thus needing a highly scalable and fast solution.

Google BigQuery, a leading Cloud-Based Data Warehousing Solution can provide an effective and unified solution for all your data scaling demands. It can handle all types of varying Workloads and can optimally allocate the compute resources to your query requests ensuring high performance. 

It is often required to export BigQuery Table to CSV for further business analysis. In this article, you will learn how to easily transfer data from your BigQuery Table to CSV format. 

Table of Content

Introduction to BigQuery

Google BigQuery Logo
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BigQuery is a Cloud-Based Data Warehouse offered by Google with excellent analytics capabilities. It is serverless, highly scalable, and can store Petabytes of data on-demand. One unique characteristic of the BigQuery architecture is the separation of Storage and Computation Resources. This provides a greater degree of flexibility as both of them can be scaled independently without the loss of performance.

Download the Cheatsheet on How to Set Up High-performance ETL to BigQuery
Download the Cheatsheet on How to Set Up High-performance ETL to BigQuery
Download the Cheatsheet on How to Set Up High-performance ETL to BigQuery
Learn the best practices and considerations for setting up high-performance ETL to BigQuery

Unlike the traditional Server rooms which required regular checks, this is a completely maintained and managed Cloud service that allows you to focus on your core business objectives. You can analyse your data at lightning speed using the Standard SQL platform with the ability to perform concurrent queries from multiple users.  

Key Features of Big Query

Since the launch of BigQuery in 2010, constant additions and enhancements have been made for better performance and ease of use. The following are some of the salient features that BigQuery possess:

  • Scalability: BigQuery employs Colossus for storage, Google’s Global Storage System. It follows the Columnar storage model with data compression for optimal storage. Owing to its architecture, Google BigQuery automatically provides any required number of resources to handle the growing data needs of your business.  
  • Real-Time Analytics: Availability of Data from all your Sources for Data Analysis in Real-time allows you to perform complex queries on the latest data and stay up to date with your business performance.
  • Google Ecosystem: You can seamlessly connect to the vast sea of tools offered by Google. Effortlessly integrate with Business Intelligence Solutions like Google Data Studio or analyse your data with Google Sheets without requiring SQL knowledge.
  • Ease-of-Use: In just a few minutes, you can start using this brilliant tool as you don’t need to deploy clusters, size your storage, or set up compression and encryption settings.
  • Machine Learning Capabilities: BigQuery ML arms you to directly build and apply ML models on petabytes of data using simple SQL commands efficiently. Carrying out Predictive Analysis in real-time helps you gain better insights into your business for making strategic decisions. 
  • Security: You can be rest assured when sharing and storing your data in Google BigQuery as data is always encrypted in both transit and rest. Automatic data backups and the 7-day change history is saved for you to restore your data. You also set access permissions for individuals to view different datasets and tables.  

For more details on these eye-catching features, you can visit the Official Google BigQuery Features page. 

Simplify ETL and Analysis Using Hevo’s No-code Data Pipeline

Hevo Data is a No-code Data Pipeline that offers a fully managed solution to set up Data Integration for 100+ Data Sources (Including 40+ Free Sources) and will let you directly load data to a data warehouse like Google BigQuery or a destination of your choice. It will automate your data flow in minutes without writing any line of code. Its fault-tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully automated solution to manage data in real-time and always have analysis-ready data. 

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Let’s look at some of the salient features of Hevo:

  • Fully Managed: It requires no management and maintenance as Hevo is a fully automated platform.
  • Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer. 
  • Real-Time: Hevo offers real-time data migration. So, your data is always ready for analysis.
  • Schema Management: Hevo can automatically detect the schema of the incoming data and maps it to the destination schema.
  • Connectors: Hevo supports 100+ integrations to SaaS platforms, files, Databases, analytics, and BI tools. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake Data Warehouses; Amazon S3 Data Lakes; and MySQL, SQL Server, TokuDB, DynamoDB, PostgreSQL Databases to name a few.  
  • Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
  • Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Live Monitoring: Advanced monitoring gives you a one-stop view to watch all the activities that occur within pipelines.
  • Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
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Prerequisites

  • An active BigQuery Account.
  • An active Google Cloud Account.

Methods to Export BigQuery Table to CSV

BigQuery doesn’t allow you to directly export table data to your local storage, Google Sheets, etc. Firstly, the data is exported to Google Cloud Storage(GCS) and from there you can download your data in CSV format to your desired destination. 

Before beginning the export process, ensure that you have the following permissions:

  • bigquery.tables.export permissions to export data from the tables.
  • bigquery.jobs.create permissions to run an export job.
  • storage.objects.create and storage.objects.delete permissions to write data in an existing Google Cloud Bucket.

There are 3 main methods to export Bigquery Table to CSV: 

  • Method 1: Export BigQuery Table to CSV using Cloud Console
  • Method 2: Export BigQuery Table to CSV using bq extract command in the bq command-line tool
  • Method 3: Export BigQuery Table to CSV by Submitting an Extract Job via Client Libraries (APIs)

Method 1: Export BigQuery Table to CSV using Cloud Console

This is the easiest way to Export BigQuery Table to CSV as it does not require any technical knowledge.  Follow the simple steps below to effortlessly Export BigQuery Table to CSV:

  • Step 1: Go to the Google Cloud Console in BigQuery. 
  • Step 2: Navigate to the Explorer panel and select the desired table from your project.
  • Step 3: From the details panel, click on the Export option and select Export to Cloud Storage.
Export BigQuery Table to CSV Dialog Box
Image Source
  • Step 4: An Export table to Google Cloud Storage dialog box will pop on your screen. Select the desired destination bucket, folder, or file for your Table Data to be stored as CSV. 
  • Step 5: From the Drop Menu in Export Format, select CSV. Also, Select None for no Compression or GZIP compression which is supported by CSV.
  • Step 6: Click on the Export button to complete the Export BigQuery Table to CSV process.

Method 2: Export BigQuery Table to CSV using bq extract command in the bq command-line tool  

Using the bq command-line tool, you can easily Export Bigquery Table to CSV by following the sample code given below:  

bq --location=location extract 
--destination_format format 
--compression compression_type 
--field_delimiter delimiter 
--print_header=boolean 
project_id:dataset.table 
gs://bucket/filename.ext

The above code snippet has the following parameters:

  • Location: The location of your BigQuery data. For example, if you are in the USA, the location is ’US.’
  • Destination Format: The format of the file you need in Google Cloud Storage(GCS).
  • Compression Type: The specific compression type supported by the file format.
  • Delimiter: For CSV, t and tab are used to indicate the boundary between columns.
  • Boolean: The default value is true allowing header rows to be printed to the exported data.  
  • Project_ID, Dataset, Table: Details of the Table you are exporting your data from.
  • Bucket, Filename.ext: The storage location of your file in GCS and filename with the desired format.

Given below is an example for exporting ‘maindataset.mytable’ in CSV filename onefile.csv with GZIP compression in a GCS bucket named bucket-one.

bq extract 
--compression GZIP 
'maindataset.mytable' 
gs:// bucket-one/onefile.csv

Method 3: Export BigQuery Table to CSV by Submitting an Extract Job via Client Libraries (APIs)

It is also possible to Export BigQuery Table to CSV format using various programming environments such as C#, Go, Java, Node.js, PHP, Python and Ruby. To begin, you need to Install the Client Libraries and then start writing queries. For instance, you can use the following sample code in Python to Export BigQuery Table to CSV:

from google.cloud import bigquery
client = bigquery.Client()
bucket_name = 'bucket-one'
project = "project-one"
dataset_id = "one"
table_id = "onefile"

destination_uri = "gs://{}/{}".format(bucket_name, "onefile.csv")
dataset_ref = bigquery.DatasetReference(project, dataset_id)
table_ref = dataset_ref.table(table_id)

extract_job = client.extract_table(
    table_ref,
    destination_uri,
    # Location must match that of the source table.
    location="US",
)  # API request
extract_job.result()  # Waits for job to complete.

print(
    "Exported {}:{}.{} to {}".format(project, dataset_id, table_id, destination_uri)
)

For more details on how to submit an Extract Job using the API and the Client Libraries, click here.

Conclusion

In this article, you learned how to effectively Export a BigQuery Table to CSV using 3 different techniques. Google BigQuery has become immensely popular among companies for their growing data storage needs. Big Query provides a reliable, secure, and scalable data warehouse with accelerated analytics.

Once you are equipped with Google BigQuery’s lightning speed for business analysis, you can make data-driven decisions and design new strategies accordingly. As your business grows, a massive amount of data is generated, associated with your customers, products, and services from various SaaS applications used for Accounting, Sales, Customer Relationship Management, Human Resources, Support, etc. Regularly updating and maintaining the flow of information can be a tedious task. You would be required to invest a part of your engineering bandwidth to Integrate, Clean, Transform and Load the data into your Google Bigquery Data Warehouse. All of this can be comfortably automated by a Cloud-Based ETL Tool like Hevo Data

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Hevo Data will seamlessly transfer your data, allowing you to focus on other aspects of your business like Analytics, Customer Management, etc. This platform allows you to efficiently transfer data from a huge collection of sources to a Data Warehouse like Google BigQuery or a destination of your choice to be visualized in a BI Tool. It is a secure and fully automated service that doesn’t require you to write any code!

If you are using Google Big Query as a Data Warehousing and Analysis platform for your business and searching for a stress-free alternative to Manual Data Integration, then Hevo can accurately automate this for you. Hevo, with its strong integration with 100+ sources & BI tools, allows you to not only export & load data but also transform & enrich your data & make it analysis-ready in a jiffy.

Want to take Hevo for a ride? Sign Up for a 14-day free trial and simplify your Data Integration process. Do check out the pricing details to understand which plan fulfills all your business needs.

Share with us your experience of setting up the process to Export BigQuery Table to CSV. Let us know in the comments section below!  

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