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

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

Connecting to Everhour Data

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

Everhour API Profile Settings

Obtain your API Key from your Everhour profile page (scroll to the bottom of the page).

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

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

Create a SQL Statement to Query Everhour

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

sql = "SELECT Id, ProjectId FROM Assignments WHERE Type = 'assignment'"

Extract, Transform, and Load the Everhour Data

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

Loading Everhour Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

sql = "SELECT Id, ProjectId FROM Assignments WHERE Type = 'assignment'"

table1 = etl.fromdb(cnxn,sql)

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

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