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

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

Connecting to SAP Concur Data

Connecting to SAP Concur 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.

SAP Concur uses the OAuth 2 authentication standard. You will need to obtain the OAuthClientId and OAuthClientSecret by registering an app with SAP Concur. See the Getting Started section of the help documentation for an authentication guide.

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

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

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query SAP Concur

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

sql = "SELECT Id, OfficeId FROM Departments WHERE Id = '1668776136772254'"

Extract, Transform, and Load the SAP Concur Data

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

Loading SAP Concur Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

In the following example, we add new rows to the Departments table.

Adding New Rows to SAP Concur

table1 = [ ['Id','OfficeId'], ['NewId1','NewOfficeId1'], ['NewId2','NewOfficeId2'], ['NewId3','NewOfficeId3'] ]

etl.appenddb(table1, cnxn, 'Departments')

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

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Id, OfficeId FROM Departments WHERE Id = '1668776136772254'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Id','OfficeId'], ['NewId1','NewOfficeId1'], ['NewId2','NewOfficeId2'], ['NewId3','NewOfficeId3'] ]

etl.appenddb(table3, cnxn, 'Departments')