Databricks Certified Data Engineer - Professional
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In the context of designing a data pipeline for a Databricks project that ingests data from multiple sources, processes it, and stores the results in a data warehouse, consider the following scenario: The project must adhere to strict compliance standards, ensure high data quality, and be scalable to handle increasing data volumes. Additionally, the solution must provide comprehensive monitoring and error handling capabilities. Given these requirements, which of the following approaches BEST meets the project's needs? (Choose one option.)
In the context of designing a data pipeline for a Databricks project that ingests data from multiple sources, processes it, and stores the results in a data warehouse, consider the following scenario: The project must adhere to strict compliance standards, ensure high data quality, and be scalable to handle increasing data volumes. Additionally, the solution must provide comprehensive monitoring and error handling capabilities. Given these requirements, which of the following approaches BEST meets the project's needs? (Choose one option.)
Explanation:
Option B is the best approach because it leverages Databricks' native capabilities to create a scalable, maintainable, and compliant data pipeline. By modularizing the pipeline into distinct stages with dedicated notebooks and jobs, it ensures clear separation of concerns, facilitates data quality checks and error handling at each step, and supports comprehensive monitoring. This design aligns with the project's requirements for scalability, compliance, and high data quality, while avoiding the pitfalls of monolithic designs, external dependencies, or excessive custom development.