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Answer: Design a multi-stage ETL pipeline with appropriate data modeling techniques, such as star schema or snowflake schema, to optimize the data model for query performance.
Option B is the correct answer. Designing a multi-stage ETL pipeline and using appropriate data modeling techniques, such as star schema or snowflake schema, allows for optimizing the data model for query performance in a data warehouse. Ignoring the data model optimization or using a single-stage ETL process may lead to suboptimal query performance. Using a traditional file-based storage system may not provide the desired scalability and performance for business intelligence and analytics.
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Your company is planning to implement a data warehousing solution to support business intelligence and analytics. Describe the steps you would take to design and implement an ETL pipeline for a data warehouse, and explain the considerations involved in optimizing the data model for query performance.
A
Use a single-stage ETL process to load all data into the data warehouse and perform all transformations and analysis there, without considering the data model optimization.
B
Design a multi-stage ETL pipeline with appropriate data modeling techniques, such as star schema or snowflake schema, to optimize the data model for query performance.
C
Use a traditional file-based storage system to store the data, as it is more cost-effective than using a data warehouse.
D
Focus only on the ETL process and ignore the data modeling aspect, as it is not relevant to query performance.
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