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Create ETL applications and real-time data pipelines for OData services in Python with petl.
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).
About OData Data Integration
CData simplifies access and integration of live OData services data. Our customers leverage CData connectivity to:
- Access OData versions 2.0, 3.0, and 4.0, working with legacy services and the latest features and capabilities.
- Leverage advanced query options, including $filter, $select, and $expand, enhancing data retrieval from 3rd party tools.
- Use Server-side execution of aggregation and grouping to minimize data transfer and boost performance.
- Authenticate securely using a variety of schemes, including Azure AD, digest, negotiate, NTLM, OAuth, and more means secure authentication with every connection.
- Use SQL stored procedures to manage OData service entities - listing, creating, and removing associations between entities.
Customers use CData's solutions to regularly integrate their OData services with preferred tools, such as Power BI, MicroStrategy, or Tableau, and to replicate data from OData services to their databases or data warehouses.
Getting Started
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')