How to Build an ETL App for XML 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 XML-connected applications and pipelines for extracting, transforming, and loading XML data. This article shows how to connect to Connect AI and use petl to extract, transform, and load XML 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 XML 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 "XML" from the Add Connection panel

3. Enter the necessary authentication properties to connect to XML.

   Connecting to Local or Cloud-Stored (Box, Google Drive, Amazon S3, SharePoint) XML Files

   CData Drivers let you work with XML files stored locally and stored in cloud storage services like Box, Amazon S3, Google Drive, or SharePoint, right where they are.

   Setting connection properties for local files

   Set the URI property to local folder path.

   Setting connection properties for files stored in Amazon S3

   To connect to XML file(s) within Amazon S3, set the URI property to the URI of the Bucket and Folder where the intended XML files exist. In addition, at least set these properties:

   • AWSAccessKey: AWS Access Key (username)
   • AWSSecretKey: AWS Secret Key

   Setting connection properties for files stored in Box

   To connect to XML file(s) within Box, set the URI property to the URI of the folder that includes the intended XML file(s). Use the OAuth authentication method to connect to Box.

   Dropbox

   To connect to XML file(s) within Dropbox, set the URI proprerty to the URI of the folder that includes the intended XML file(s). Use the OAuth authentication method to connect to Dropbox. Either User Account or Service Account can be used to authenticate.

   SharePoint Online (SOAP)

   To connect to XML file(s) within SharePoint with SOAP Schema, set the URI proprerty to the URI of the document library that includes the intended XML file. Set User, Password, and StorageBaseURL.

   SharePoint Online REST

   To connect to XML file(s) within SharePoint with REST Schema, set the URI proprerty to the URI of the document library that includes the intended XML file. StorageBaseURL is optional. If not set, the driver will use the root drive. OAuth is used to authenticate.

   Google Drive

   To connect to XML file(s) within Google Drive, set the URI property to the URI of the folder that includes the intended XML file(s). Use the OAuth authentication method to connect and set InitiateOAuth to GETANDREFRESH.

   The DataModel property is the controlling property over how your data is represented into tables and toggles the following basic configurations.

   • Document (default): Model a top-level, document view of your XML data. The data provider returns nested elements as aggregates of data.
   • FlattenedDocuments: Implicitly join nested documents and their parents into a single table.
   • Relational: Return individual, related tables from hierarchical data. The tables contain a primary key and a foreign key that links to the parent document.

   See the Modeling XML Data chapter for more information on configuring the relational representation. You will also find the sample data used in the following examples. The data includes entries for people, the cars they own, and various maintenance services performed on those cars.

   
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 XML 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 XML

Use SQL to create a statement for querying XML. In this article, we read data from the people entity. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, XML1).

sql = (
    "SELECT [ personal.name.first ], [ personal.name.last ] "
    "FROM [XML1].[XML].[people] "
    "WHERE [ personal.name.last ] = 'Roberts'"
)

Extract, Transform, and Load the XML Data

With a connection and query in hand, use petl to extract, transform, and load the XML data. In this example, we extract XML data, sort the data by the [ personal.name.last ] column, and load the data into a CSV file.

table1 = etl.fromdb(conn, sql)

table2 = etl.sort(table1, '[ personal.name.last ]')

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

Load New Rows Back into XML

When XML supports writes, load rows back with a batch INSERT. The SDK's executemany takes @name placeholders and a list of parameter dictionaries, one per row.

cur = conn.cursor()
cur.executemany(
    "INSERT INTO [XML1].[XML].[people] ([ personal.name.first ], [ personal.name.last ]) "
    "VALUES (@val1, @val2)",
    [
        {"@val1": "New value 1", "@val2": "New value 1"},
        {"@val1": "New value 2", "@val2": "New value 2"},
    ],
)
print(f"Rows inserted: {cur.rowcount}")

conn.close()

Note: Even for writable sources, a read-only PAT or connection permission will reject write operations.

With the CData Connect AI Python SDK, you can work with XML 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 XML data through petl using the CData Connect AI Python SDK. For more information on connecting to XML (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live XML 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 [ personal.name.first ], [ personal.name.last ] "
    "FROM [XML1].[XML].[people] "
    "WHERE [ personal.name.last ] = 'Roberts'"
)

table1 = etl.fromdb(conn, sql)

table2 = etl.sort(table1, '[ personal.name.last ]')

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

cur = conn.cursor()
cur.executemany(
    "INSERT INTO [XML1].[XML].[people] ([ personal.name.first ], [ personal.name.last ]) "
    "VALUES (@val1, @val2)",
    [
        {"@val1": "New value 1", "@val2": "New value 1"},
        {"@val1": "New value 2", "@val2": "New value 2"},
    ],
)
print(f"Rows inserted: {cur.rowcount}")
conn.close()