Utilizing the BigQuery I/O Connector within your Dataflow pipeline to read and process tabular data directly from BigQuery.

Implementing a custom tf.data.Dataset reader that leverages the BigQuery Storage API to fetch data directly into your TensorFlow model.

Using the BigQuery Python client library to query data and load it into a Pandas DataFrame for further processing in your machine learning models.

Employing a tf.data.Iterator to directly iterate over BigQuery tables without prior data loading or transformation steps.

Combining both the BigQuery Python client library for initial data exploration and the BigQuery I/O Connector for scalable data processing in Dataflow.