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

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

Connecting to Snowflake Data

Connecting to Snowflake 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 connect to Snowflake:

1. Set User and Password to your Snowflake credentials and set the AuthScheme property to PASSWORD or OKTA.
2. Set URL to the URL of the Snowflake instance (i.e.: https://myaccount.snowflakecomputing.com).
3. Set Warehouse to the Snowflake warehouse.
4. (Optional) Set Account to your Snowflake account if your URL does not conform to the format above.
5. (Optional) Set Database and Schema to restrict the tables and views exposed.

See the Getting Started guide in the CData driver documentation for more information.

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

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

cnxn = mod.connect("User=Admin;Password=test123;Server=localhost;Database=Northwind;Warehouse=TestWarehouse;Account=Tester1;")

Create a SQL Statement to Query Snowflake

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

sql = "SELECT Id, ProductName FROM Products WHERE Id = '1'"

Extract, Transform, and Load the Snowflake Data

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

Loading Snowflake 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 Snowflake

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

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

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

cnxn = mod.connect("User=Admin;Password=test123;Server=localhost;Database=Northwind;Warehouse=TestWarehouse;Account=Tester1;")

sql = "SELECT Id, ProductName FROM Products WHERE Id = '1'"

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')