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

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

Connecting to Landbot Data

Connecting to Landbot 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.

Using API Key Authentication

Landbot uses token-based authentication. Obtain your agent token from Settings > Account in your Landbot account.

Set the following connection properties:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Landbot agent token.

Sample Connection String

Profile=C:\profiles\Landbot.apip;AuthScheme=APIKey;APIKey=your_agent_token_here;

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

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

cnxn = mod.connect("Profile=C:\profiles\Landbot.apip;AuthScheme=APIKey;APIKey=your_agent_token_here;")

Create a SQL Statement to Query Landbot

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

sql = "SELECT ,  FROM Agents WHERE  = ''"

Extract, Transform, and Load the Landbot Data

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

Loading Landbot Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

With the CData API Driver for Python, you can work with Landbot 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 API Driver for Python to start building Python apps and scripts with connectivity to Landbot 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.api as mod

cnxn = mod.connect("Profile=C:\profiles\Landbot.apip;AuthScheme=APIKey;APIKey=your_agent_token_here;")

sql = "SELECT ,  FROM Agents WHERE  = ''"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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