Databricks Certified Data Engineer - Professional
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In a scenario where you are required to process data from multiple sources, perform complex transformations, and load the results into a data warehouse using Databricks, which of the following approaches would be the MOST efficient and scalable? Consider factors such as modularity, error handling, maintainability, and the ability to handle dependencies between tasks. Choose the best option from the four provided.
In a scenario where you are required to process data from multiple sources, perform complex transformations, and load the results into a data warehouse using Databricks, which of the following approaches would be the MOST efficient and scalable? Consider factors such as modularity, error handling, maintainability, and the ability to handle dependencies between tasks. Choose the best option from the four provided.
Explanation:
Option D is the most efficient and scalable approach because it allows for parallel processing of data from different sources, significantly reducing the total processing time. Writing all transformed data to a common data warehouse ensures data consistency and simplifies data management. Configuring tasks to run in parallel, where possible, optimizes resource utilization and job performance, making this approach superior in terms of scalability and efficiency.