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

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

Connecting to Abyssale Data

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

Abyssale API Profile Settings

Abyssale uses API Key authentication. Your Abyssale API Key is sent as the x-api-key request header. You can generate or view your API Key in the Abyssale dashboard under Settings > API.

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

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Abyssale API Key.

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

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

Create a SQL Statement to Query Abyssale

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

sql = "SELECT Id, Name FROM Designs WHERE Type = 'static'"

Extract, Transform, and Load the Abyssale Data

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

Loading Abyssale Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

sql = "SELECT Id, Name FROM Designs WHERE Type = 'static'"

table1 = etl.fromdb(cnxn,sql)

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

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