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

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

Connecting to Databox Data

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

Databox API Profile Settings

To use the Databox API, you need to generate a personal access key from your Databox account. Navigate to the Databox account settings under Data Manager and generate an API key (prefixed with pak_).

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

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Databox personal access key.

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

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

Create a SQL Statement to Query Databox

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

sql = "SELECT Id, Title FROM Datasets WHERE DataSourceId = '4976164'"

Extract, Transform, and Load the Databox Data

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

Loading Databox Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

sql = "SELECT Id, Title FROM Datasets WHERE DataSourceId = '4976164'"

table1 = etl.fromdb(cnxn,sql)

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

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