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

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

Connecting to OData Services

Connecting to OData services 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 User and Password properties, under the Authentication section, must be set to valid OData user credentials. In addition, you will need to specify a URL to a valid OData server organization root or OData services file.

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

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

cnxn = mod.connect("URL=http://services.odata.org/V4/Northwind/Northwind.svc;UseIdUrl=True;OData Version=4.0;Data Format=ATOM;")

Create a SQL Statement to Query OData

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

sql = "SELECT OrderName, Freight FROM Orders WHERE ShipCity = 'New York'"

Extract, Transform, and Load the OData Services

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

Loading OData Services 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 OData

table1 = [ ['OrderName','Freight'], ['NewOrderName1','NewFreight1'], ['NewOrderName2','NewFreight2'], ['NewOrderName3','NewFreight3'] ]

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

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

Full Source Code

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

cnxn = mod.connect("URL=http://services.odata.org/V4/Northwind/Northwind.svc;UseIdUrl=True;OData Version=4.0;Data Format=ATOM;")

sql = "SELECT OrderName, Freight FROM Orders WHERE ShipCity = 'New York'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['OrderName','Freight'], ['NewOrderName1','NewFreight1'], ['NewOrderName2','NewFreight2'], ['NewOrderName3','NewFreight3'] ]

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