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

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

Connecting to MySQL Data

Connecting to MySQL 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.

The Server and Port properties must be set to a MySQL server. If IntegratedSecurity is set to false, then User and Password must be set to valid user credentials. Optionally, Database can be set to connect to a specific database. If not set, tables from all databases will be returned.

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

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

cnxn = mod.connect("User=myUser;Password=myPassword;Database=NorthWind;Server=myServer;Port=3306;")

Create a SQL Statement to Query MySQL

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

sql = "SELECT ShipName, Freight FROM Orders WHERE ShipCountry = 'USA'"

Extract, Transform, and Load the MySQL Data

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

Loading MySQL Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to MySQL

table1 = [ ['ShipName','Freight'], ['NewShipName1','NewFreight1'], ['NewShipName2','NewFreight2'], ['NewShipName3','NewFreight3'] ]

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

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

cnxn = mod.connect("User=myUser;Password=myPassword;Database=NorthWind;Server=myServer;Port=3306;")

sql = "SELECT ShipName, Freight FROM Orders WHERE ShipCountry = 'USA'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['ShipName','Freight'], ['NewShipName1','NewFreight1'], ['NewShipName2','NewFreight2'], ['NewShipName3','NewFreight3'] ]

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