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Understanding Google Cloud BigQuery Storage API v2.10.0: A Comprehensive Guide

Google Cloud BigQuery is a fast, scalable, and cost-effective multi-cloud data warehouse that enables super-fast SQL queries using the processing power of Google’s infrastructure. Version 2.10.0 of the BigQuery Storage API provides several important features and improvements that can be leveraged for various data analysis and machine learning tasks.

Features of google-cloud-bigquery-storage==2.10.0

1. Enhanced Performance

  • Arrow Format Support: Allows BigQuery to return query results in the Apache Arrow format, which is optimized for in-memory columnar data processing.
  • Optimized Reading: The API provides mechanisms for reading from managed tables and materialized views with increased efficiency and lower latency.

2. Increased Flexibility

  • Schema Evolution Support: This version supports changes in table schema over time without the need to modify client code.
  • Compatibility with Different Libraries: It can be easily integrated with various data processing libraries such as Pandas, TensorFlow, and PyTorch.

3. Security Enhancements

  • Fine-Grained Access Control: The API supports identity and access management (IAM) roles, giving admins the ability to control who can view and modify data within BigQuery.

How to Use the BigQuery Storage API v2.10.0


To get started, you’ll need to install the library by running:

pip install google-cloud-bigquery-storage==2.10.0


Create a BigQuery Client: This will be used to perform operations on BigQuery.

from import bigquery
client = bigquery.Client()

Use the BigQuery Storage Client: The storage client allows you to perform various operations related to reading and writing data.

from import BigQueryStorageClient
storage_client = BigQueryStorageClient()

Query Data: You can now query data from BigQuery and read the results using the storage API.

query = "SELECT * FROM `freshers_in.viewdataset.users_id`"
rows = client.query(query)
for row in rows:

Advantages of Using google-cloud-bigquery-storage==2.10.0

  1. Speed: By leveraging the Arrow format and optimized reading mechanisms, data retrieval becomes much faster.
  2. Ease of Integration: Seamlessly integrate with other tools, frameworks, and libraries.
  3. Scalability: Ideal for handling large datasets without any performance degradation.
  4. Cost-Effectiveness: Efficient reading mechanisms lead to cost savings when dealing with large-scale data analytics.

BigQuery import urls to refer

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