Extract, Transform, and Load Sybase IQ Data in Python

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Sybase IQ Python Connector

Python Connector Libraries for Sybase IQ Data Connectivity. Integrate Sybase IQ with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



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

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

Connecting to Sybase IQ Data

Connecting to Sybase IQ 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 SybaseIQ, set User, Password, Server and Database properties. To secure connections with TLS/SSL, set UseSSL to TRUE.

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

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

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

Create a SQL Statement to Query Sybase IQ

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

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

Extract, Transform, and Load the Sybase IQ Data

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

Loading Sybase IQ Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Sybase IQ

table1 = [ ['ProductName','Price'], ['NewProductName1','NewPrice1'], ['NewProductName2','NewPrice2'], ['NewProductName3','NewPrice3'] ]

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

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

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

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

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['ProductName','Price'], ['NewProductName1','NewPrice1'], ['NewProductName2','NewPrice2'], ['NewProductName3','NewPrice3'] ]

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