How to Build an ETL App for Google Spanner Data in Python with CData

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

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

Connecting to Google Spanner Data

Connecting to Google Spanner 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 Spanner uses the OAuth authentication standard. To authenticate using OAuth, you can use the embedded credentials or register an app with Google.

See the Getting Started guide in the CData driver documentation for more information.

After installing the CData Google Spanner Connector, follow the procedure below to install the other required modules and start accessing Google Spanner 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 Google Spanner 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.googlespanner as mod

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

cnxn = mod.connect("ProjectId='project1';InstanceId='instance1';Database='db1';InitiateOAuth=GETANDREFRESH;")

Create a SQL Statement to Query Google Spanner

Use SQL to create a statement for querying Google Spanner. In this article, we read data from the Customer entity.

sql = "SELECT Name, TotalDue FROM Customer WHERE Id = '1'"

Extract, Transform, and Load the Google Spanner Data

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

Loading Google Spanner Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

etl.tocsv(table2,'customer_data.csv')

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

Adding New Rows to Google Spanner

table1 = [ ['Name','TotalDue'], ['NewName1','NewTotalDue1'], ['NewName2','NewTotalDue2'], ['NewName3','NewTotalDue3'] ]

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

With the CData Python Connector for Google Spanner, you can work with Google Spanner 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 CData Python Connector for Google Spanner to start building Python apps and scripts with connectivity to Google Spanner 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.googlespanner as mod

cnxn = mod.connect("ProjectId='project1';InstanceId='instance1';Database='db1';InitiateOAuth=GETANDREFRESH;")

sql = "SELECT Name, TotalDue FROM Customer WHERE Id = '1'"

table1 = etl.fromdb(cnxn,sql)

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

etl.tocsv(table2,'customer_data.csv')

table3 = [ ['Name','TotalDue'], ['NewName1','NewTotalDue1'], ['NewName2','NewTotalDue2'], ['NewName3','NewTotalDue3'] ]

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