How to set up BigQuery Export? 3 Easy Ways

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, and enhances scalability, and performance as opposed to traditional On-premise Data Warehousing systems. Google BigQuery is one of the well-known and widely accepted Cloud-based Data Warehouse Applications.

In this article, you will gain information about BigQuery Export. You will also gain a holistic understanding of Google BigQuery, its key features and the steps to be followed to set up BigQuery Export. Read along to find out in-depth information about setting up BigQuery Export.

Table of Contents

• Introduction to Google BigQuery
  • Key Features of Google BigQuery
    • Performance
    • Scalability
    • Security
    • Usability
    • Data Types
    • Data Loading
    • Integrations
    • Data Recovery
    • Pricing Models
• BigQuery Export: Permissions Required
  • BigQuery Export: BigQuery Permissions
  • BigQuery Export: Cloud Storage Permissions
• BigQuery Export Set-up: 3 Methods
  • BigQuery Export Method 1: Using the Cloud Console
  • BigQuery Export Method 2: Using the bq command-line tool
  • BigQuery Export Method 3: Using the API or client libraries
• Limitations for Setting Up BigQuery Export
• Conclusion

Introduction to Google BigQuery

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. To study more about Google BigQuery, refer to the following document.

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

Key Features of Google BigQuery

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

• Performance
• Scalability
• Security
• Usability
• Data Types
• Data Loading
• Integrations
• Data Recovery
• Pricing Models

1) Performance

Partitioning is supported by BigQuery, which improves Query performance. The data may be readily queried using SQL or Open Database Connectivity (ODBC). 

2) Scalability

Being quite elastic, BigQuery separates computation and storage, allowing customers to scale processing and memory resources according to their needs. The tool has significant vertical and horizontal scalability and runs real-time queries on petabytes of data in a very short period.

3) Security

When a third-party authorization exists, users can utilize OAuth as a standard approach to get the cluster. By default, all data is encrypted and in transit. Cloud Identity and Access Management (IAM) allows for fine-tuning administration.

4) Usability

Google BigQuery is a highly user-friendly platform that requires a basic understanding of SQL commands, ETL tools, etc.

5) Data Types

 It supports JSON and XML file formats.

6) Data Loading

It employs the conventional ELT/ETL Batch Data Loading techniques by employing standard SQL dialect, as well as Data Streaming to load data row by row using Streaming APIs. To know about ETL vs ELT, click here.

7) Integrations

In addition to operational databases, the system supports integration with a wide range of Data Integration Tools, Business Intelligence (BI), and Artificial Intelligence (AI) solutions. It also works with Google Workspace and Cloud Platform.

8) Data Recovery

Data backup and disaster recovery are among the services provided by Google BigQuery. Users can query point-in-time snapshots of data changes from the last seven days.

9) 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.

BigQuery Export: Permissions Required

For conducting BigQuery export to Cloud Storage, several permissions are required to access the BigQuery table that contains the data, permissions to run an export job, and permissions to write the data to the Cloud Storage bucket. To learn about BigQuery IAM Management, defining Permissions & Access Controls, click here.

The permissions required for conducting BigQuery Export are as follows:

• BigQuery Export: BigQuery Permissions
• BigQuery Export: Cloud Storage Permissions

1) BigQuery Permissions

• At a minimum, to export data, you must be granted bigquery.tables.export permissions. The predefined IAM roles that are granted bigquery.tables.export permissions are as follows:
  • bigquery.dataViewer
  • bigquery.dataOwner
  • bigquery.dataEditor
  • bigquery.admin
• At a minimum, to run a BigQuery Export job, you must have bigquery.jobs.create permissions granted. The predefined IAM roles that are granted bigquery.jobs.create permissions are as follows:
  • bigquery.user
  • bigquery.jobUser
  • bigquery.admin

2) Cloud Storage Permissions

• To write the data to an existing Cloud Storage bucket, you must be granted storage.objects.create and storage.objects.delete permissions. The predefined IAM roles that grant both the permissions are as follows:
  • storage.objectAdmin
  • storage.admin

BigQuery Export Set-up: 3 Methods

In the following article, we are going to learn about the 3 easy methods for exporting Google BigQuery table data. The methods for setting up BigQuery Export are as follows:

• BigQuery Export Method 1: Using the Google Cloud Console
• BigQuery Export Method 2: Using the bq command-line tool
• BigQuery Export Method 3: Using the API or client libraries

Method 1: BigQuery Export using the Cloud Console

Google Cloud Console provides a means to export BigQuery data to a Google Cloud Storage bucket in CSV, JSON, or Apache Avro formats.

The steps followed to set up BigQuery Export using Cloud Console are as follows:

• Step 1: Login into your Google Cloud account.
• Step 2: Open the BigQuery page in the Cloud Console.
• Step 3: In the left navigation panel, expand a Project and dataset to list the schemas. In the Explorer panel, expand your project and dataset.

• Step 4: Now, Click on a table to view its details.

• Step 5: In the details panel, click on “Export” and select “Export to Cloud Storage“.

• Step 6: In the Export table to Google Cloud Storage dialog:
  • For Select Google Cloud Storage location, browse for the bucket, folder, or file where you want to export the data.
  • For Export format, choose the format for your exported data: CSV, JSON (Newline Delimited), Avro, or Parquet.
  • For Compression, select a compression format or select “None” for no compression.
  • Click on “Export” to export the table.

To check on the progress of the job, look near the top of the navigation for Job history for an Export job.

For further information about setting up BigQuery Export using Google Cloud Console, follow the Official Documentation.

Method 2: BigQuery Export using the Command-Line Client bq

Cloud Console is the easiest method of working directly with BigQuery datasets but it doesn’t provide many levels of in-depth control to the user.  A great alternative that offers flexibility, without having to code a solution from scratch to interact with BigQuery, is the bq command-line client.

The Google Cloud SDK installation includes the command-line client bq. You can follow the Official Guide for the bq command-line tool.

The bq command-line client provides a number of features for interacting with BigQuery such as loading, exporting, creating tables, and retrieving information about datasets. For a full list of documented features and usage, review Google reference for Command-line tool reference.

A) Basic Syntax of using bq

bq --common_flags <bq_command> --command-specific_flags <command_arguments>

B) To Display Basic Table Schema info

bq show publicdata:samples.julius

C) To Display Basic Table Schema info without Data in JSON format (unformatted)

bq show --format=json publicdata:samples.julius

D) To Display Basic Table Schema info without Data in JSON format (formatted)

bq show --format=prettyjson publicdata:samples.julius

E) Redirecting bq Output to a File

Most terminals and shells allow you to save files of most generated text by using the > operator. For example, to save the basic schema of a BigQuery table to a JSON file, just add “>” to the command followed by the filename.

bq show --format=json publicdata:samples.julius > julius.json

F) To Export SQL Query result to a local JSON File

bq --format=prettyjson query --n=1000 "SELECT * from publicdata:samples.julius" > export.json

For further information about using setting up BigQuery Export using the bq command-line tool, you can follow the Official Documentation.

Method 3: BigQuery Export by using API or Client Library (i.e., C#, Go, Java, Python, PHP)

A) Using API

• You create an extract job and populate the job configuration.
• Specify your location in the location property in the jobReference section of the job resource (This is optional).

The steps followed to set up BigQuery Export using API are as follows:

• Step 1: First, create an extract job that points to the BigQuery source data and the Cloud Storage destination.
• Step 2: Now, specify the source table by using the sourceTable configuration object that contains the project ID, dataset ID, and table ID.
• Step 3: The destination URI(s) property must be according to the format gs://bucket/filename.ext. Each URI can contain one ‘*‘ wildcard character and it must appear after the bucket name.
• Step 4: Specify the data format by setting the configuration.extract.destinationFormat property. For example, to export a JSON file, set this property to the value NEWLINE_DELIMITED_JSON.
• Step 5: To check the job status, you can call jobs.get(job_id) with the ID of the job returned by the initial request.
  • If status.state = DONE, the job is completed successfully.
  • If the status.errorResult property is present, the request failed, and that object will include information explaining what went wrong.
  • If status.errorResult is absent, the job has finished successfully, although there might have been some non-fatal errors. Non-fatal errors are listed in the returned job object’s status.errors property.

For further information about setting up BigQuery Export using APIs, follow the Official Documentation.

B) Using Client Libraries

There are numerous Google BigQuery API client libraries for popular programming languages that are available and officially supported by Google, such as C#, Go, Java, and Python, providing programmatic data access to BigQuery-hosted datasets.

For detailed information about the individual steps followed for setting up BigQuery Export using different Client Libraries, you can follow the Official Documentation.

You can also follow the Avro Export and Parquet Export details.

Limitations for setting up BigQuery Export

When you conduct BigQuery Export, there are several things that should be kept in mind:

• A BigQuery table data cannot be exported to a local file, Sheets, or Drive. Cloud Storage is the only BigQuery export destination that is supported. For information on saving query results, you can see Downloading and saving query results.
• Up to 1 GB of table data can be exported to a single file. If you need to export more than 1 GB of data, use a wildcard to export and split it up into numerous files. When you export data to numerous files, the file sizes will vary.
• Nested and repeated data cannot be exported in CSV format. Avro, JSON, and Parquet exports enable nested and repeated data.
• When you export data in JSON format, INT64 (integer) data types are encoded as JSON strings to maintain 64-bit precision when read by other systems.
• You cannot export data from multiple tables in a single BigQuery export job.
• When exporting data from the Cloud Console, you cannot select a compression type other than GZIP.
• When you export data to a Cloud Storage bucket that has a configured retention policy, BigQuery may fail to write the files to the bucket. You can Relax the retention policy for the length of the export jobs.
• When you export a table in JSON format, the symbols, <,>, and & are transformed to Unicode notation uNNNN, where N is a hexadecimal digit. profit&loss, for example, becomes profitu0026loss. This Unicode conversion is performed to avoid security vulnerabilities.

Conclusion

In this article, you have learned about the steps involved in setting up Google BigQuery Export. We’ve also learned about Google BigQuery and its key features.

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