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Get the Report →How to Build an ETL App for Snowflake Data in Python with CData
Create ETL applications and real-time data pipelines for Snowflake data in Python with petl.
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).
About Snowflake Data Integration
CData simplifies access and integration of live Snowflake data. Our customers leverage CData connectivity to:
- Reads and write Snowflake data quickly and efficiently.
- Dynamically obtain metadata for the specified Warehouse, Database, and Schema.
- Authenticate in a variety of ways, including OAuth, OKTA, Azure AD, Azure Managed Service Identity, PingFederate, private key, and more.
Many CData users use CData solutions to access Snowflake from their preferred tools and applications, and replicate data from their disparate systems into Snowflake for comprehensive warehousing and analytics.
For more information on integrating Snowflake with CData solutions, refer to our blog: https://www.cdata.com/blog/snowflake-integrations.
Getting Started
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:
- Set User and Password to your Snowflake credentials and set the AuthScheme property to PASSWORD or OKTA.
- Set URL to the URL of the Snowflake instance (i.e.: https://myaccount.snowflakecomputing.com).
- Set Warehouse to the Snowflake warehouse.
- (Optional) Set Account to your Snowflake account if your URL does not conform to the format above.
- (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')