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

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

Connecting to Tanda Data

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

Tanda API Profile Settings

Register an OAuth application at the Tanda applications page by clicking New App and entering your app name and redirect URI.

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

cnxn = mod.connect("Profile=C:\profiles\Tanda.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

Create a SQL Statement to Query Tanda

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

sql = "SELECT ShiftId, UserId FROM ActiveShifts WHERE Status = 'clocked_in'"

Extract, Transform, and Load the Tanda Data

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

Loading Tanda Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Tanda 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 Tanda 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\Tanda.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

sql = "SELECT ShiftId, UserId FROM ActiveShifts WHERE Status = 'clocked_in'"

table1 = etl.fromdb(cnxn,sql)

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

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