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

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

Connecting to Azure Table Data

Connecting to Azure Table 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.

Specify your AccessKey and your Account to connect. Set the Account property to the Storage Account Name and set AccessKey to one of the Access Keys. Either the Primary or Secondary Access Keys can be used. To obtain these values, navigate to the Storage Accounts blade in the Azure portal. You can obtain the access key by selecting your account and clicking Access Keys in the Settings section.

After installing the CData Azure Table Connector, follow the procedure below to install the other required modules and start accessing Azure Table 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 Azure Table 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.azuretables as mod

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

cnxn = mod.connect("AccessKey=myAccessKey;Account=myAccountName;")

Create a SQL Statement to Query Azure Table

Use SQL to create a statement for querying Azure Table. In this article, we read data from the NorthwindProducts entity.

sql = "SELECT Name, Price FROM NorthwindProducts WHERE ShipCity = 'New York'"

Extract, Transform, and Load the Azure Table Data

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

Loading Azure Table Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Azure Table

table1 = [ ['Name','Price'], ['NewName1','NewPrice1'], ['NewName2','NewPrice2'], ['NewName3','NewPrice3'] ]

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

With the CData Python Connector for Azure, you can work with Azure Table 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 Azure to start building Python apps and scripts with connectivity to Azure Table 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.azuretables as mod

cnxn = mod.connect("AccessKey=myAccessKey;Account=myAccountName;")

sql = "SELECT Name, Price FROM NorthwindProducts WHERE ShipCity = 'New York'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','Price'], ['NewName1','NewPrice1'], ['NewName2','NewPrice2'], ['NewName3','NewPrice3'] ]

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