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

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

Connecting to TimeCamp Data

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

TimeCamp API Profile Settings

TimeCamp uses token-based authentication. To obtain an API Token:

1. Log in to your TimeCamp account at https://app.timecamp.com
2. Click your profile avatar in the top-right corner
3. Select Profile Settings
4. Copy the API token shown at the bottom of the page

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

• AuthScheme: Set this to APIKey.

Set the following in the ProfileSettings connection property:

• APIKey: Set this to your TimeCamp API token.

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

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

Create a SQL Statement to Query TimeCamp

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

sql = "SELECT UserId, Email FROM GroupUsers WHERE GroupId = '12345'"

Extract, Transform, and Load the TimeCamp Data

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

Loading TimeCamp Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

sql = "SELECT UserId, Email FROM GroupUsers WHERE GroupId = '12345'"

table1 = etl.fromdb(cnxn,sql)

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

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