![]() 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. In the following example, we add new rows to the Orders table. In this example, we extract MySQL data, sort the data by the Freight column, and load the data into a CSV file. With the query results stored in a DataFrame, we can use petl to extract, transform, and load the MySQL data. Sql = "SELECT ShipName, Freight FROM Orders WHERE ShipCountry = 'USA'"Įxtract, Transform, and Load the MySQL Data In this article, we read data from the Orders entity. Use SQL to create a statement for querying MySQL. Use the connect function for the CData MySQL Connector to create a connection for working with MySQL data.Ĭnxn = mod.connect("User=myUser Password=myPassword Database=NorthWind Server=myServer Port=3306 ") You can now connect with a connection string. Code snippets follow, but the full source code is available at the end of the article.įirst, be sure to import the modules (including the CData Connector) with the following: Once the required modules and frameworks are installed, we are ready to build our ETL app. Pip install pandas Build an ETL App for MySQL Data in Python Use the pip utility to install the required modules and frameworks: pip install petl If not set, tables from all databases will be returned.Īfter installing the CData MySQL Connector, follow the procedure below to install the other required modules and start accessing MySQL through Python objects. Optionally, Database can be set to connect to a specific database. If IntegratedSecurity is set to false, then User and Password must be set to valid userĬredentials. The Server and Port properties must be set to a MySQL server. For this article, you will pass the connection string as a parameter to the create_engine function. Create a connection string using the required connection properties. ![]() 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).Ĭonnecting to MySQL data looks just like connecting to any relational data source. ![]() With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live MySQL data in Python. 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 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. The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively.
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