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

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

Connecting to SAP Business One Data

Connecting to SAP Business One 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 to SAP Business One you must provide the and properties.

To connect to data, specify . This is your SAP Business One Service Layer root URL.

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

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

cnxn = mod.connect("Url=http://localhost:50000/b1s/v1;User=username;Password=password;CompanyDB=dbname;InitiateOAuth=GETANDREFRESH;")

Create a SQL Statement to Query SAP Business One

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

sql = "SELECT DocEntry, DocType FROM Orders WHERE DocType = 'dDocument_Items'"

Extract, Transform, and Load the SAP Business One Data

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

Loading SAP Business One Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to SAP Business One

table1 = [ ['DocEntry','DocType'], ['NewDocEntry1','NewDocType1'], ['NewDocEntry2','NewDocType2'], ['NewDocEntry3','NewDocType3'] ]

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

With the CData Python Connector for SAP Business One, you can work with SAP Business One 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 Business One to start building Python apps and scripts with connectivity to SAP Business One data. Reach out to our Support Team if you have any questions.

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Full Source Code

import petl as etl
import pandas as pd
import cdata.sapbusinessone as mod

cnxn = mod.connect("Url=http://localhost:50000/b1s/v1;User=username;Password=password;CompanyDB=dbname;InitiateOAuth=GETANDREFRESH;")

sql = "SELECT DocEntry, DocType FROM Orders WHERE DocType = 'dDocument_Items'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['DocEntry','DocType'], ['NewDocEntry1','NewDocType1'], ['NewDocEntry2','NewDocType2'], ['NewDocEntry3','NewDocType3'] ]

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