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

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

Connecting to Parallel Data

Connecting to Parallel 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.

The Parallel API uses API Key authentication via the x-api-key request header.

Using API Key Authentication

Your Parallel API key is required to create a connection. To obtain your API key:

1. Log into your Parallel account at app.parallel.ai.
2. Navigate to Settings or API Keys in your account dashboard.
3. Generate or copy your API key.

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

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

Example connection string:

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

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

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

Create a SQL Statement to Query Parallel

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

sql = "SELECT ,  FROM MonitorEvents WHERE MonitorId = 'mon_abc123'"

Extract, Transform, and Load the Parallel Data

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

Loading Parallel Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

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

sql = "SELECT ,  FROM MonitorEvents WHERE MonitorId = 'mon_abc123'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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