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

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

Connecting to Azure Data Catalog Data

Connecting to Azure Data Catalog 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.

You can optionally set the following to read the different catalog data returned from Azure Data Catalog.

Connect Using OAuth Authentication

You must use OAuth to authenticate with Azure Data Catalog. OAuth requires the authenticating user to interact with Azure Data Catalog using the browser. For more information, refer to the OAuth section in the help documentation.

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

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;")

Create a SQL Statement to Query Azure Data Catalog

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

sql = "SELECT DslAddressDatabase, Type FROM Tables WHERE Name = 'FactProductInventory'"

Extract, Transform, and Load the Azure Data Catalog Data

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

Loading Azure Data Catalog Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData Python Connector for Azure Data Catalog, you can work with Azure Data Catalog 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 Data Catalog to start building Python apps and scripts with connectivity to Azure Data Catalog data. Reach out to our Support Team if you have any questions.

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Full Source Code

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;")

sql = "SELECT DslAddressDatabase, Type FROM Tables WHERE Name = 'FactProductInventory'"

table1 = etl.fromdb(cnxn,sql)

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

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