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Extract, Transform, and Load IBM Cloud SQL Query Data in Python

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

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

Connecting to IBM Cloud SQL Query Data

Connecting to IBM Cloud SQL Query 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.

IBM Cloud SQL uses the OAuth and HMAC authentication standards. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

After installing the CData IBM Cloud SQL Query Connector, follow the procedure below to install the other required modules and start accessing IBM Cloud SQL Query 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 IBM Cloud SQL Query 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.ibmcloudsqlquery as mod

You can now connect with a connection string. Use the connect function for the CData IBM Cloud SQL Query Connector to create a connection for working with IBM Cloud SQL Query data.

cnxn = mod.connect("Api Key=MyAPIKey;Instance CRN=myInstanceCRN;Region=myRegion;Schema=mySchema;OAuth Client Id=myOAuthClientId;OAuth Client Secret=myOAuthClientSecret;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query IBM Cloud SQL Query

Use SQL to create a statement for querying IBM Cloud SQL Query. In this article, we read data from the Jobs entity.

sql = "SELECT Id, Status FROM Jobs WHERE UserId = 'user@domain.com'"

Extract, Transform, and Load the IBM Cloud SQL Query Data

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

Loading IBM Cloud SQL Query Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to IBM Cloud SQL Query

table1 = [ ['Id','Status'], ['NewId1','NewStatus1'], ['NewId2','NewStatus2'], ['NewId3','NewStatus3'] ]

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

With the CData Python Connector for IBM Cloud SQL Query, you can work with IBM Cloud SQL Query 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 IBM Cloud SQL Query Python Connector to start building Python apps and scripts with connectivity to IBM Cloud SQL Query 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.ibmcloudsqlquery as mod

cnxn = mod.connect("Api Key=MyAPIKey;Instance CRN=myInstanceCRN;Region=myRegion;Schema=mySchema;OAuth Client Id=myOAuthClientId;OAuth Client Secret=myOAuthClientSecret;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Id, Status FROM Jobs WHERE UserId = 'user@domain.com'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Id','Status'], ['NewId1','NewStatus1'], ['NewId2','NewStatus2'], ['NewId3','NewStatus3'] ]

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