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

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

Connecting to IBM Cloud Object Storage Data

Connecting to IBM Cloud Object Storage 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.

Register a New Instance of Cloud Object Storage

If you do not already have Cloud Object Storage in your IBM Cloud account, follow the procedure below to install an instance of SQL Query in your account:

1. Log in to your IBM Cloud account.
2. Navigate to the page, choose a name for your instance and click Create. You will be redirected to the instance of Cloud Object Storage you just created.

Connecting using OAuth Authentication

There are certain connection properties you need to set before you can connect. You can obtain these as follows:

API Key

To connect with IBM Cloud Object Storage, you need an API Key. You can obtain this as follows:

1. Log in to your IBM Cloud account.
2. Navigate to the Platform API Keys page.
3. On the middle-right corner click "Create an IBM Cloud API Key" to create a new API Key.
4. In the pop-up window, specify the API Key name and click "Create". Note the API Key as you can never access it again from the dashboard.

Cloud Object Storage CRN

If you have multiple accounts, you will need to specify the CloudObjectStorageCRN explicitly. To find the appropriate value, you can:

• Query the Services view. This will list your IBM Cloud Object Storage instances along with the CRN for each.
• Locate the CRN directly in IBM Cloud. To do so, navigate to your IBM Cloud Dashboard. In the Resource List, Under Storage, select your Cloud Object Storage resource to get its CRN.

Connecting to Data

You can now set the following to connect to data:

• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
• ApiKey: Set this to your API key which was noted during setup.
• CloudObjectStorageCRN (Optional): Set this to the cloud object storage CRN you want to work with. While the connector attempts to retrieve this automatically, specifying this explicitly is recommended if you have more than Cloud Object Storage account.

When you connect, the connector completes the OAuth process.

1. Extracts the access token and authenticates requests.
2. Saves OAuth values in OAuthSettingsLocation to be persisted across connections.

After installing the CData IBM Cloud Object Storage Connector, follow the procedure below to install the other required modules and start accessing IBM Cloud Object Storage 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 Object Storage 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.ibmcloudobjectstorage as mod

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

cnxn = mod.connect("ApiKey=myApiKey;CloudObjectStorageCRN=MyInstanceCRN;Region=myRegion;OAuthClientId=MyOAuthClientId;OAuthClientSecret=myOAuthClientSecret;")

Create a SQL Statement to Query IBM Cloud Object Storage

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

sql = "SELECT Key, Etag FROM Objects WHERE Bucket = 'someBucket'"

Extract, Transform, and Load the IBM Cloud Object Storage Data

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

Loading IBM Cloud Object Storage Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData Python Connector for IBM Cloud Object Storage, you can work with IBM Cloud Object Storage 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 IBM Cloud Object Storage to start building Python apps and scripts with connectivity to IBM Cloud Object Storage 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.ibmcloudobjectstorage as mod

cnxn = mod.connect("ApiKey=myApiKey;CloudObjectStorageCRN=MyInstanceCRN;Region=myRegion;OAuthClientId=MyOAuthClientId;OAuthClientSecret=myOAuthClientSecret;")

sql = "SELECT Key, Etag FROM Objects WHERE Bucket = 'someBucket'"

table1 = etl.fromdb(cnxn,sql)

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

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