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

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

Connecting to MongoDB Data

Connecting to MongoDB 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.

Set the Server, Database, User, and Password connection properties to connect to MongoDB. To access MongoDB collections as tables you can use automatic schema discovery or write your own schema definitions. Schemas are defined in .rsd files, which have a simple format. You can also execute free-form queries that are not tied to the schema.

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

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

cnxn = mod.connect("Server=MyServer;Port=27017;Database=test;User=test;Password=Password;")

Create a SQL Statement to Query MongoDB

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

sql = "SELECT borough, cuisine FROM restaurants WHERE Name = 'Morris Park Bake Shop'"

Extract, Transform, and Load the MongoDB Data

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

Loading MongoDB Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to MongoDB

table1 = [ ['borough','cuisine'], ['Newborough1','Newcuisine1'], ['Newborough2','Newcuisine2'], ['Newborough3','Newcuisine3'] ]

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

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

cnxn = mod.connect("Server=MyServer;Port=27017;Database=test;User=test;Password=Password;")

sql = "SELECT borough, cuisine FROM restaurants WHERE Name = 'Morris Park Bake Shop'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['borough','cuisine'], ['Newborough1','Newcuisine1'], ['Newborough2','Newcuisine2'], ['Newborough3','Newcuisine3'] ]

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