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

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

Connecting to Lakebase Data

Connecting to Lakebase 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 Databricks Lakebase, start by setting the following properties:

• DatabricksInstance: The Databricks instance or server hostname, provided in the format instance-abcdef12-3456-7890-abcd-abcdef123456.database.cloud.databricks.com.
• Server: The host name or IP address of the server hosting the Lakebase database.
• Port (optional): The port of the server hosting the Lakebase database, set to 5432 by default.
• Database (optional): The database to connect to after authenticating to the Lakebase Server, set to the authenticating user's default database by default.

OAuth Client Authentication

To authenicate using OAuth client credentials, you need to configure an OAuth client in your service principal. In short, you need to do the following:

1. Create and configure a new service principal
2. Assign permissions to the service principal
3. Create an OAuth secret for the service principal

For more information, refer to the Setting Up OAuthClient Authentication section in the Help documentation.

OAuth PKCE Authentication

To authenticate using the OAuth code type with PKCE (Proof Key for Code Exchange), set the following properties:

• AuthScheme: OAuthPKCE.
• User: The authenticating user's user ID.

For more information, refer to the Help documentation.

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

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

cnxn = mod.connect("DatabricksInstance=lakebase;Server=127.0.0.1;Port=5432;Database=my_database;InitiateOAuth=GETANDREFRESH;")

Create a SQL Statement to Query Lakebase

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

sql = "SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = 'USA'"

Extract, Transform, and Load the Lakebase Data

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

Loading Lakebase Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Lakebase

table1 = [ ['ShipName','ShipCity'], ['NewShipName1','NewShipCity1'], ['NewShipName2','NewShipCity2'], ['NewShipName3','NewShipCity3'] ]

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

With the CData Python Connector for Lakebase, you can work with Lakebase 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 Lakebase to start building Python apps and scripts with connectivity to Lakebase data. Reach out to our Support Team if you have any questions.

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Full Source Code

import petl as etl
import pandas as pd
import cdata.lakebase as mod

cnxn = mod.connect("DatabricksInstance=lakebase;Server=127.0.0.1;Port=5432;Database=my_database;InitiateOAuth=GETANDREFRESH;")

sql = "SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = 'USA'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['ShipName','ShipCity'], ['NewShipName1','NewShipCity1'], ['NewShipName2','NewShipCity2'], ['NewShipName3','NewShipCity3'] ]

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