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

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

Connecting to Cosmos DB Data

Connecting to Cosmos DB 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.

To obtain the connection string needed to connect to a Cosmos DB account using the SQL API, log in to the Azure Portal, select Azure Cosmos DB, and select your account. In the Settings section, click Connection String and set the following values:

• AccountEndpoint: The Cosmos DB account URL from the Keys blade of the Cosmos DB account
• AccountKey: In the Azure portal, navigate to the Cosmos DB service and select your Azure Cosmos DB account. From the resource menu, go to the Keys page. Find the PRIMARY KEY value and set AccountKey to this value.

After installing the CData Cosmos DB Connector, follow the procedure below to install the other required modules and start accessing Cosmos DB 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 Cosmos DB 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.cosmosdb as mod

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

cnxn = mod.connect("AccountEndpoint=myAccountEndpoint;AccountKey=myAccountKey;")

Create a SQL Statement to Query Cosmos DB

Use SQL to create a statement for querying Cosmos DB. In this article, we read data from the Customers entity.

sql = "SELECT City, CompanyName FROM Customers WHERE Name = 'Morris Park Bake Shop'"

Extract, Transform, and Load the Cosmos DB Data

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

Loading Cosmos DB Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Cosmos DB

table1 = [ ['City','CompanyName'], ['NewCity1','NewCompanyName1'], ['NewCity2','NewCompanyName2'], ['NewCity3','NewCompanyName3'] ]

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

With the CData Python Connector for Cosmos DB, you can work with Cosmos DB 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 Cosmos DB to start building Python apps and scripts with connectivity to Cosmos DB 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.cosmosdb as mod

cnxn = mod.connect("AccountEndpoint=myAccountEndpoint;AccountKey=myAccountKey;")

sql = "SELECT City, CompanyName FROM Customers WHERE Name = 'Morris Park Bake Shop'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['City','CompanyName'], ['NewCity1','NewCompanyName1'], ['NewCity2','NewCompanyName2'], ['NewCity3','NewCompanyName3'] ]

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