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

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

Connecting to Superchat Data

Connecting to Superchat 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.

Superchat uses API Key authentication. The API key is passed via the X-API-KEY request header on every call.

Authentication

To authenticate to Superchat, you need to obtain your API key from the Superchat workspace settings.

Using API Key Authentication

You can obtain your API key from Settings > Integrations > API Key in your Superchat workspace.

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Superchat API key.

Example connection string:

Profile=C:\profiles\Superchat.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key';

After installing the CData Superchat Connector, follow the procedure below to install the other required modules and start accessing Superchat 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 Superchat 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 Superchat Connector to create a connection for working with Superchat data.

cnxn = mod.connect("Profile=C:\profiles\Superchat.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key';")

Create a SQL Statement to Query Superchat

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

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

Extract, Transform, and Load the Superchat Data

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

Loading Superchat Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

With the CData API Driver for Python, you can work with Superchat 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 Superchat 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.api as mod

cnxn = mod.connect("Profile=C:\profiles\Superchat.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key';")

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

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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