Extract, Transform, and Load BigQuery Data in Python

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Python Connector Libraries for Google BigQuery Data Connectivity. Integrate Google BigQuery with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

The CData Python Connector for BigQuery enables you to create ETL applications and pipelines for BigQuery data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for BigQuery and the petl framework, you can build BigQuery-connected applications and pipelines for extracting, transforming, and loading BigQuery data. This article shows how to connect to BigQuery with the CData Python Connector and use petl and pandas to extract, transform, and load BigQuery data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live BigQuery data in Python. When you issue complex SQL queries from BigQuery, the driver pushes supported SQL operations, like filters and aggregations, directly to BigQuery and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to BigQuery Data

Connecting to BigQuery data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Google uses the OAuth authentication standard. To access Google APIs on behalf of individual users, you can use the embedded credentials or you can register your own OAuth app.

OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, you will need to register an application to obtain the OAuth JWT values.

In addition to the OAuth values, you will need to specify the DatasetId and ProjectId. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

After installing the CData BigQuery Connector, follow the procedure below to install the other required modules and start accessing BigQuery through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for BigQuery Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.googlebigquery as mod

You can now connect with a connection string. Use the connect function for the CData BigQuery Connector to create a connection for working with BigQuery data.

cnxn = mod.connect("DataSetId=MyDataSetId;ProjectId=MyProjectId;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query BigQuery

Use SQL to create a statement for querying BigQuery. In this article, we read data from the Orders entity.

sql = "SELECT OrderName, Freight FROM Orders WHERE ShipCity = 'New York'"

Extract, Transform, and Load the BigQuery Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the BigQuery data. In this example, we extract BigQuery data, sort the data by the Freight column, and load the data into a CSV file.

Loading BigQuery Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Freight')


In the following example, we add new rows to the Orders table.

Adding New Rows to BigQuery

table1 = [ ['OrderName','Freight'], ['NewOrderName1','NewFreight1'], ['NewOrderName2','NewFreight2'], ['NewOrderName3','NewFreight3'] ]

etl.appenddb(table1, cnxn, 'Orders')

With the CData Python Connector for BigQuery, you can work with BigQuery data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the BigQuery Python Connector to start building Python apps and scripts with connectivity to BigQuery data. Reach out to our Support Team if you have any questions.

Full Source Code

import petl as etl
import pandas as pd
import cdata.googlebigquery as mod

cnxn = mod.connect("DataSetId=MyDataSetId;ProjectId=MyProjectId;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT OrderName, Freight FROM Orders WHERE ShipCity = 'New York'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Freight')


table3 = [ ['OrderName','Freight'], ['NewOrderName1','NewFreight1'], ['NewOrderName2','NewFreight2'], ['NewOrderName3','NewFreight3'] ]

etl.appenddb(table3, cnxn, 'Orders')