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Extract, Transform, and Load JSON Services in Python

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

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

Connecting to JSON Services

Connecting to JSON 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.

See the Getting Started chapter in the data provider documentation to authenticate to your data source: The data provider models JSON APIs as bidirectional database tables and 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 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 JSON 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 JSON 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 JSON Connector, follow the procedure below to install the other required modules and start accessing JSON 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 JSON 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.json as mod

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

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

Create a SQL Statement to Query JSON

Use SQL to create a statement for querying JSON. 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 JSON Services

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

Loading JSON Services 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 JSON

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 JSON, you can work with JSON 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 JSON Python Connector to start building Python apps and scripts with connectivity to JSON 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.json as mod

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

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')