Extract, Transform, and Load Databricks Data in Python

Ready to get started?

Download for a free trial:

Download Now

Learn more:

Databricks Python Connector

Python Connector Libraries for Databricks Data Connectivity. Integrate Databricks with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



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

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

Connecting to Databricks Data

Connecting to Databricks 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.

To connect to a Databricks cluster, set the properties as described below.

Note: The needed values can be found in your Databricks instance by navigating to Clusters, and selecting the desired cluster, and selecting the JDBC/ODBC tab under Advanced Options.

  • Server: Set to the Server Hostname of your Databricks cluster.
  • HTTPPath: Set to the HTTP Path of your Databricks cluster.
  • Token: Set to your personal access token (this value can be obtained by navigating to the User Settings page of your Databricks instance and selecting the Access Tokens tab).

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

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

cnxn = mod.connect("Server=127.0.0.1;Port=443;TransportMode=HTTP;HTTPPath=MyHTTPPath;UseSSL=True;User=MyUser;Password=MyPassword;")

Create a SQL Statement to Query Databricks

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

sql = "SELECT City, CompanyName FROM Customers WHERE Country = 'US'"

Extract, Transform, and Load the Databricks Data

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

Loading Databricks Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Databricks

table1 = [ ['City','CompanyName'], ['NewCity1','NewCompanyName1'], ['NewCity2','NewCompanyName2'], ['NewCity3','NewCompanyName3'] ]

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

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

cnxn = mod.connect("Server=127.0.0.1;Port=443;TransportMode=HTTP;HTTPPath=MyHTTPPath;UseSSL=True;User=MyUser;Password=MyPassword;")

sql = "SELECT City, CompanyName FROM Customers WHERE Country = 'US'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['City','CompanyName'], ['NewCity1','NewCompanyName1'], ['NewCity2','NewCompanyName2'], ['NewCity3','NewCompanyName3'] ]

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