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

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

Connecting to SAP Fieldglass Data

Connecting to SAP Fieldglass 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.

To authenticate, you will need to specify the Username, Password, APIKey, and EnvironmentURL connection properties.

To obtain an APIKey, log in to the SAP API Business Hub and click on Get API Key.

After installing the CData SAP Fieldglass Connector, follow the procedure below to install the other required modules and start accessing SAP Fieldglass 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 SAP Fieldglass 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.sapfieldglass as mod

You can now connect with a connection string. Use the connect function for the CData SAP Fieldglass Connector to create a connection for working with SAP Fieldglass data.

cnxn = mod.connect("EnvironmentURL='https://myinstance.com';Username=myuser;Password=mypassword;APIKey=xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query SAP Fieldglass

Use SQL to create a statement for querying SAP Fieldglass. In this article, we read data from the AuditTrails entity.

sql = "SELECT Id, Category FROM AuditTrails WHERE Company = 'CData'"

Extract, Transform, and Load the SAP Fieldglass Data

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

Loading SAP Fieldglass Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData Python Connector for SAP Fieldglass, you can work with SAP Fieldglass 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 Python Connector for SAP Fieldglass to start building Python apps and scripts with connectivity to SAP Fieldglass 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.sapfieldglass as mod

cnxn = mod.connect("EnvironmentURL='https://myinstance.com';Username=myuser;Password=mypassword;APIKey=xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Id, Category FROM AuditTrails WHERE Company = 'CData'"

table1 = etl.fromdb(cnxn,sql)

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

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