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Extract, Transform, and Load REST Data in Python

The CData Python Connector for REST enables you to create ETL applications and pipelines for REST data 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 REST and the petl framework, you can build REST-connected applications and pipelines for extracting, transforming, and loading REST data. This article shows how to connect to REST with the CData Python Connector and use petl and pandas to extract, transform, and load REST data.

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

Connecting to REST Data

Connecting to REST 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.

See the Getting Started chapter in the data provider documentation to authenticate to your data source: The data provider models REST APIs as bidirectional database tables and XML/JSON files as read-only views (local files, files stored on popular cloud services, and FTP servers). The major authentication schemes are supported, including HTTP Basic, Digest, NTLM, OAuth, and FTP. See the Getting Started chapter in the data provider documentation for authentication guides.

After setting the URI and providing any authentication values, set Format to "XML" or "JSON" and set DataModel to more closely match the data representation to the structure of your data.

The DataModel property is the controlling property over how your data is represented into tables and toggles the following basic configurations.

  • Document (default): Model a top-level, document view of your REST data. The data provider returns nested elements as aggregates of data.
  • FlattenedDocuments: Implicitly join nested documents and their parents into a single table.
  • Relational: Return individual, related tables from hierarchical data. The tables contain a primary key and a foreign key that links to the parent document.

See the Modeling REST Data chapter for more information on configuring the relational representation. You will also find the sample data used in the following examples. The data includes entries for people, the cars they own, and various maintenance services performed on those cars.

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

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

cnxn = mod.connect("DataModel=Relational;URI=C:\people.xml;Format=XML;")

Create a SQL Statement to Query REST

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

sql = "SELECT [ personal.name.first ], [ personal.name.last ] FROM people WHERE [ personal.name.last ] = 'Roberts'"

Extract, Transform, and Load the REST Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the REST data. In this example, we extract REST data, sort the data by the [ personal.name.last ] column, and load the data into a CSV file.

Loading REST Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'[ personal.name.last ]')

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

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

Adding New Rows to REST

table1 = [ ['[ personal.name.first ]','[ personal.name.last ]'], ['New[ personal.name.first ]1','New[ personal.name.last ]1'], ['New[ personal.name.first ]2','New[ personal.name.last ]2'], ['New[ personal.name.first ]3','New[ personal.name.last ]3'] ]

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

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

cnxn = mod.connect("DataModel=Relational;URI=C:\people.xml;Format=XML;")

sql = "SELECT [ personal.name.first ], [ personal.name.last ] FROM people WHERE [ personal.name.last ] = 'Roberts'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'[ personal.name.last ]')

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

table3 = [ ['[ personal.name.first ]','[ personal.name.last ]'], ['New[ personal.name.first ]1','New[ personal.name.last ]1'], ['New[ personal.name.first ]2','New[ personal.name.last ]2'], ['New[ personal.name.first ]3','New[ personal.name.last ]3'] ]

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