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

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

Connecting to Bannerbear Data

Connecting to Bannerbear 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 Bannerbear Profile on disk (e.g. C:\profiles\Bannerbear.apip). Next, set the ProfileSettings connection property to the connection string for Bannerbear (see below).

Bannerbear API Profile Settings

Retrieve your API keys from the Bannerbear dashboard. Specify the key type (Project or Master) via the KeyType property.

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

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

Create a SQL Statement to Query Bannerbear

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

sql = "SELECT Uid, CreatedAt FROM Account WHERE PaidPlanName = 'premium'"

Extract, Transform, and Load the Bannerbear Data

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

Loading Bannerbear Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

sql = "SELECT Uid, CreatedAt FROM Account WHERE PaidPlanName = 'premium'"

table1 = etl.fromdb(cnxn,sql)

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

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