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

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

Connecting to PostgreSQL Data

Connecting to PostgreSQL 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 PostgreSQL, set the Server, Port (the default port is 5432), and Database connection properties and set the User and Password you wish to use to authenticate to the server. If the Database property is not specified, the data provider connects to the user's default database.

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

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

cnxn = mod.connect("User=postgres;Password=admin;Database=postgres;Server=127.0.0.1;Port=5432;")

Create a SQL Statement to Query PostgreSQL

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

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

Extract, Transform, and Load the PostgreSQL Data

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

Loading PostgreSQL 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 PostgreSQL

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

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

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

cnxn = mod.connect("User=postgres;Password=admin;Database=postgres;Server=127.0.0.1;Port=5432;")

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')