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

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

Connecting to MariaDB Data

Connecting to MariaDB 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 MariaDB 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, the tables from all databases are reported.

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

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

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

Create a SQL Statement to Query MariaDB

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

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

Extract, Transform, and Load the MariaDB Data

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

Loading MariaDB Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to MariaDB

table1 = [ ['ShipName','ShipCity'], ['NewShipName1','NewShipCity1'], ['NewShipName2','NewShipCity2'], ['NewShipName3','NewShipCity3'] ]

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

With the CData Python Connector for MariaDB, you can work with MariaDB 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 MariaDB to start building Python apps and scripts with connectivity to MariaDB data. Reach out to our Support Team if you have any questions.

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Full Source Code

import petl as etl
import pandas as pd
import cdata.mariadb as mod

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

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

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['ShipName','ShipCity'], ['NewShipName1','NewShipCity1'], ['NewShipName2','NewShipCity2'], ['NewShipName3','NewShipCity3'] ]

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