How to Build an ETL App for Google Directory 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 Directory and the petl framework, you can build Google Directory-connected applications and pipelines for extracting, transforming, and loading Google Directory data. This article shows how to connect to Google Directory with the CData Python Connector and use petl and pandas to extract, transform, and load Google Directory data.

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

Connecting to Google Directory Data

Connecting to Google Directory 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. You can authorize the data provider to access Google Spreadsheets as an individual user or with a Google Apps Domain service account. See the Getting Started section of the data provider help documentation for an authentication guide.

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

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

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Google Directory

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

sql = "SELECT Id, Description FROM MyTable WHERE Status = 'confirmed'"

Extract, Transform, and Load the Google Directory Data

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

Loading Google Directory Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Google Directory

table1 = [ ['Id','Description'], ['NewId1','NewDescription1'], ['NewId2','NewDescription2'], ['NewId3','NewDescription3'] ]

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

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

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Id, Description FROM MyTable WHERE Status = 'confirmed'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Id','Description'], ['NewId1','NewDescription1'], ['NewId2','NewDescription2'], ['NewId3','NewDescription3'] ]

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