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Answer: Construct separate pipelines for each data type, storing them in separate tables within the lakehouse and applying distinct schemas tailored to each data type's structure and requirements.
Option D is the most effective strategy because it addresses the need for cost efficiency, compliance, and scalability. Separate pipelines allow for optimized processing of each data type, reducing resource consumption. Storing data in separate tables with tailored schemas ensures compliance with data governance by maintaining data integrity and facilitates efficient querying and analysis. This approach also supports scalability, as it simplifies the addition of new data types or sources in the future.
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As a Microsoft Fabric Analytics Engineer Associate, you are designing a data pipeline to ingest data from multiple sources into a lakehouse. The data includes structured data from a SQL database, semi-structured data from a NoSQL database, and unstructured data from a file system. Considering the need for cost efficiency, compliance with data governance policies, and scalability for future data growth, which approach would you recommend? (Choose one option.)
A
Develop a unified pipeline that processes all data types together and stores them in a single table within the lakehouse, applying a generic schema to accommodate all data formats.
B
Implement separate pipelines for each data type, storing them in a single table in the lakehouse but using a different schema for each data type to maintain data integrity.
C
Design a single pipeline that handles all data types, storing them in separate tables within the lakehouse with a unified schema to simplify data access.
D
Construct separate pipelines for each data type, storing them in separate tables within the lakehouse and applying distinct schemas tailored to each data type's structure and requirements.
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