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

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

Connecting to Sybase Data

Connecting to Sybase 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 connect to Sybase, specify the following connection properties:

• Server: Set this to the name or network address of the Sybase database instance.
• Database: Set this to the name of the Sybase database running on the specified Server.

Optionally, you can also secure your connections with TLS/SSL by setting UseSSL to true.

Sybase supports several methods for authentication including Password and Kerberos.

Connect Using Password Authentication

Set the AuthScheme to Password and set the following connection properties to use Sybase authentication.

• User: Set this to the username of the authenticating Sybase user.
• Password: Set this to the username of the authenticating Sybase user.

Connect using LDAP Authentication

To connect with LDAP authentication, you will need to configure Sybase server-side to use the LDAP authentication mechanism.

After configuring Sybase for LDAP, you can connect using the same credentials as Password authentication.

Connect Using Kerberos Authentication

To leverage Kerberos authentication, begin by enabling it setting AuthScheme to Kerberos. See the Using Kerberos section in the Help documentation for more information on using Kerberos authentication.

You can find an example connection string below: Server=MyServer;Port=MyPort;User=SampleUser;Password=SamplePassword;Database=MyDB;Kerberos=true;KerberosKDC=MyKDC;KerberosRealm=MYREALM.COM;KerberosSPN=server-name

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

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

cnxn = mod.connect("User=myuser;Password=mypassword;Server=localhost;Database=mydatabase;Charset=iso_1;")

Create a SQL Statement to Query Sybase

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

sql = "SELECT Id, ProductName FROM Products WHERE ProductName = 'Konbu'"

Extract, Transform, and Load the Sybase Data

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

Loading Sybase Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Sybase

table1 = [ ['Id','ProductName'], ['NewId1','NewProductName1'], ['NewId2','NewProductName2'], ['NewId3','NewProductName3'] ]

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

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

cnxn = mod.connect("User=myuser;Password=mypassword;Server=localhost;Database=mydatabase;Charset=iso_1;")

sql = "SELECT Id, ProductName FROM Products WHERE ProductName = 'Konbu'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Id','ProductName'], ['NewId1','NewProductName1'], ['NewId2','NewProductName2'], ['NewId3','NewProductName3'] ]

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