How to Build an ETL App for IBM Cloud Object Storage Data in Python with CData Connect AI

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Connect AI Python SDK 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 Connect AI and use petl to extract, transform, and load IBM Cloud Object Storage data.

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so petl can read directly from the SDK connection with etl.fromdb. There is no driver to install per source: connect with a Personal Access Token and build your pipeline.

Connect to IBM Cloud Object Storage in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "IBM Cloud Object Storage" from the Add Connection panel

3. Enter the necessary authentication properties to connect to IBM Cloud Object Storage.

   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, 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.
   
4. Click Save & Test
5. Navigate to the Permissions tab and update the user-based permissions.

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK and the petl framework using the pip utility:

pip install cdata-connect-ai
pip install petl

Build an ETL App for IBM Cloud Object Storage Data in Python

Once the required modules are installed, you are ready to build the ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, import the modules and connect to Connect AI with your account email and PAT:

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

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. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, IBMCloudObjectStorage1).

sql = (
    "SELECT Key, Etag "
    "FROM [IBMCloudObjectStorage1].[IBMCloudObjectStorage].[Objects] "
    "WHERE Bucket = 'someBucket'"
)

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

With a connection and query in hand, 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.

table1 = etl.fromdb(conn, sql)

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

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

IBM Cloud Object Storage is a read-only source in Connect AI, so this pipeline can extract and transform IBM Cloud Object Storage data but not load rows back. Close the connection when the extract is complete:

conn.close()

With the CData Connect AI Python SDK, 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.

More Information and Free Trial

Now you can pipe live IBM Cloud Object Storage data through petl using the CData Connect AI Python SDK. For more information on connecting to IBM Cloud Object Storage (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live IBM Cloud Object Storage data in Python.

Full Source Code

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

sql = (
    "SELECT Key, Etag "
    "FROM [IBMCloudObjectStorage1].[IBMCloudObjectStorage].[Objects] "
    "WHERE Bucket = 'someBucket'"
)

table1 = etl.fromdb(conn, sql)

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

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