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

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

Connecting to Guru Data

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

Guru API Profile Settings

In Guru, navigate to Settings > API and click Create New API Token. Use your email address as the User and the generated token as the Password.

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

cnxn = mod.connect("Profile=C:\profiles\Guru.apip;ProfileSettings='User=your_email;Password=your_api_token';")

Create a SQL Statement to Query Guru

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

sql = "SELECT User, Type FROM Analytics WHERE TeamId = 'team-12345'"

Extract, Transform, and Load the Guru Data

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

Loading Guru Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Guru 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 Guru 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\Guru.apip;ProfileSettings='User=your_email;Password=your_api_token';")

sql = "SELECT User, Type FROM Analytics WHERE TeamId = 'team-12345'"

table1 = etl.fromdb(cnxn,sql)

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

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