Develop a monolithic Databricks notebook that combines data ingestion, processing, and storage operations, with inline data quality checks and error handling. This approach simplifies the pipeline structure but may lack scalability and detailed monitoring.

Design a modular data pipeline using separate Databricks notebooks for each stage (ingestion, processing, storage), linked together via Databricks jobs. Each notebook includes specific data quality checks, error handling, and performance metrics logging, enabling scalability and easier maintenance.

Leverage a third-party ETL tool for data ingestion and initial processing, then use Databricks notebooks for further processing and storage. While this approach may offer some built-in data quality features, it introduces additional complexity and potential compliance risks due to external dependencies.

Build a custom pipeline using Apache Spark jobs outside of Databricks, with a separate data quality framework and monitoring solution. This approach offers maximum flexibility but requires significant development effort and may not fully leverage Databricks' integrated features.