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 find it challenging to run large queries or apply Machine Learning to study a trend, thus needing a highly scalable and fast solution.
Google BigQuery can provide an effective and unified solution for all your data scaling demands. It can handle all types of varying Workloads and optimally allocate the compute resources to your query requests, ensuring high performance.
It is often required to export data from BigQuery to CSV for further business analysis. In this article, you will learn how to easily export data from BigQuery Tables in CSV format.
Methods to Export BigQuery Table to CSV
The option to download BigQuery table as a CSV file offers a valuable solution for users seeking flexibility and accessibility. 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 BigQuery Table to CSV process, ensure that you have the following permissions to execute the BigQuery export to CSV operation::
- 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.
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- 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.
Limitations of this method
- Table data cannot be exported to Google Sheets, Google Drive, or a local file. Cloud Storage is the only supported export location.
- There is no assurance about the produced file size when utilising the EXPORT DATA statement.
- An export task may create a variable number of files.
- INT64 (integer) data types are encoded as JSON strings when exported in JSON format in order to maintain 64-bit accuracy when the data is read by other systems.
- A single export task cannot export data from more than one table.
- When you export data via the Google Cloud interface, you are limited to the GZIP compression option.
- Unless you utilize the EXPORT DATA statement and include an ORDER BY clause in the query_statement, the order of the exported table data is not guaranteed.
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 execute the BigQuery Export query results 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 the filename with the desired format.
Given below is an example of 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 Python 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 to execute BigQuery Export Query Results to CSV Python:
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.
Before wrapping up, let’s cover some basics.
Introduction to BigQuery
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 losing performance.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 analyze your data at lightning speed using the Standard SQL platform with the ability to perform concurrent queries from multiple users.
Key Features of BigQuery
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 possesses:
- 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: The 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 analyze 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 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.
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. This can be comfortably automated by a Cloud-Based ETL Tool like Hevo Data. Hevo is the only real-time ELT No-code data pipeline platform that cost-effectively automates data pipelines that are flexible to your needs
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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 150+ data sources, allows you to not only export & load data but also transform & enrich your data & make it analysis-ready in a jiffy.
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Share with us your experience of setting up the process to Export BigQuery Table to CSV. Let us know in the comments section below!
Sanchit Agarwal is a data analyst at heart with a passion for data, software architecture, and writing technical content. He has experience writing more than 200 articles on data integration and infrastructure. He finds joy in breaking down complex concepts in simple and easy language, especially related to data base migration techniques and challenges in data replication.