Скачать или смотреть Google BigQuery python client creating managing jobs

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Google BigQuery is a fully-managed, serverless data warehouse that enables super-fast SQL queries using the processing power of Google's infrastructure. In this tutorial, we will explore how to use the Google BigQuery Python Client to create and manage jobs. Jobs in BigQuery represent the unit of work, such as running a SQL query, importing/exporting data, or copying tables.
Before you begin, make sure you have the following:
Google Cloud Platform Account: You need a Google Cloud Platform (GCP) account with BigQuery API enabled.
Google Cloud SDK: Install the Google Cloud SDK to authenticate and interact with GCP services. You can download it here.
Python: Ensure you have Python installed on your machine. You can download it here.
BigQuery Python Client: Install the BigQuery Python client library using the following pip command:
Before using the BigQuery Python Client, you need to authenticate your application. The simplest way is to use the Google Cloud SDK credentials. Make sure you have gcloud installed, and run:
This command opens a browser window, allowing you to authenticate your application.
Now, let's dive into creating and managing jobs using the BigQuery Python Client.
This example demonstrates how to:
This is a basic example, and depending on your use case, you might need to configure additional options such as handling large result sets, using query parameters, or dealing with external data sources.
Congratulations! You've learned how to use the Google BigQuery Python Client to create and manage jobs. This tutorial provides a foundation for more advanced use cases, such as working with large datasets, scheduling queries, and optimizing query performance. Explore the official documentation for more details and advanced features.
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