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

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

Connecting to Deepgram Data

Connecting to Deepgram 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.

Start by setting the Profile connection property to the location of the Deepgram Profile on disk (e.g. C:\profiles\Deepgram.apip). Next, set the ProfileSettings connection property to the connection string for Deepgram (see below).

Deepgram API Profile Settings

Deepgram uses API key authentication for all endpoints. API keys are scoped to a project and carry permission scopes (member, administrator, or owner). Create and manage API keys in the Deepgram console.

After obtaining your API key, set the following connection properties:

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

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

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

Create a SQL Statement to Query Deepgram

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

sql = "SELECT ApiKeyId, MemberEmail FROM Keys WHERE ProjectId = 'your-project-id'"

Extract, Transform, and Load the Deepgram Data

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

Loading Deepgram Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Deepgram 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 Deepgram 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\Deepgram.apip;AuthScheme=APIKey;ProfileSettings='APIKey=YOUR_DEEPGRAM_API_KEY';")

sql = "SELECT ApiKeyId, MemberEmail FROM Keys WHERE ProjectId = 'your-project-id'"

table1 = etl.fromdb(cnxn,sql)

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

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