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Extract, Transform, and Load SAP HANA Data in Python

The CData Python Connector for SAP HANA enables you to create ETL applications and pipelines for SAP HANA data in Python with petl.

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

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

Connecting to SAP HANA Data

Connecting to SAP HANA 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.

Set the Server, Database and Port properties to specify the address of your SAP Hana database to interact with. Set the User and the Password properties to authenticate to the server.

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

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

cnxn = mod.connect("User=system;Password=mypassword;Server=localhost;Database=systemdb;")

Create a SQL Statement to Query SAP HANA

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

sql = "SELECT Name, OwnerId FROM Buckets WHERE Name = 'TestBucket'"

Extract, Transform, and Load the SAP HANA Data

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

Loading SAP HANA Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to SAP HANA

table1 = [ ['Name','OwnerId'], ['NewName1','NewOwnerId1'], ['NewName2','NewOwnerId2'], ['NewName3','NewOwnerId3'] ]

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

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

cnxn = mod.connect("User=system;Password=mypassword;Server=localhost;Database=systemdb;")

sql = "SELECT Name, OwnerId FROM Buckets WHERE Name = 'TestBucket'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','OwnerId'], ['NewName1','NewOwnerId1'], ['NewName2','NewOwnerId2'], ['NewName3','NewOwnerId3'] ]

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