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Extract, Transform, and Load MarkLogic Data in Python

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

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

Connecting to MarkLogic Data

Connecting to MarkLogic 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 User, Password, and Server to the credentials for the MarkLogic account and the address of the server you want to connect to. You should also specify the REST API Port if you want to use a specific instance of a REST Server.

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

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

cnxn = mod.connect("User='myusername';Password='mypassword';Server='http://marklogic';")

Create a SQL Statement to Query MarkLogic

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

sql = "SELECT Name, TotalDue FROM Customer WHERE Id = '1'"

Extract, Transform, and Load the MarkLogic Data

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

Loading MarkLogic Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to MarkLogic

table1 = [ ['Name','TotalDue'], ['NewName1','NewTotalDue1'], ['NewName2','NewTotalDue2'], ['NewName3','NewTotalDue3'] ]

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

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

cnxn = mod.connect("User='myusername';Password='mypassword';Server='http://marklogic';")

sql = "SELECT Name, TotalDue FROM Customer WHERE Id = '1'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','TotalDue'], ['NewName1','NewTotalDue1'], ['NewName2','NewTotalDue2'], ['NewName3','NewTotalDue3'] ]

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