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

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

Connecting to Azure Synapse Data

Connecting to Azure Synapse 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.

Connecting to Azure Synapse

In addition to providing authentication (see below), set the following properties to connect to a Azure Synapse database:

• Server: The server running Azure. You can find this by logging into the Azure portal and navigating to Azure Synapse Analytics -> Select your database -> Overview -> Server name.
• Database: The name of the database, as seen in the Azure portal on the Azure Synapse Analytics page.

Authenticating to Azure Synapse

Connect to Azure Synapse using the following properties:

• User: The username provided for authentication with Azure.
• Password: The password associated with the authenticating user.

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

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

cnxn = mod.connect("User=myuser;Password=mypassword;Server=localhost;Database=Northwind;")

Create a SQL Statement to Query Azure Synapse

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

sql = "SELECT Id, ProductName FROM Products WHERE ProductName = 'Konbu'"

Extract, Transform, and Load the Azure Synapse Data

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

Loading Azure Synapse Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Azure Synapse

table1 = [ ['Id','ProductName'], ['NewId1','NewProductName1'], ['NewId2','NewProductName2'], ['NewId3','NewProductName3'] ]

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

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

cnxn = mod.connect("User=myuser;Password=mypassword;Server=localhost;Database=Northwind;")

sql = "SELECT Id, ProductName FROM Products WHERE ProductName = 'Konbu'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Id','ProductName'], ['NewId1','NewProductName1'], ['NewId2','NewProductName2'], ['NewId3','NewProductName3'] ]

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