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Answer: Transform the financial transaction data to match the data format and schema of the other sources before loading it into the data warehouse, ensuring data consistency and enabling real-time analytics.
Option A is the correct choice because transforming the financial transaction data to match the data format and schema of the other sources ensures data consistency within the data warehouse, which is crucial for accurate analytics. This approach also supports real-time analytics, meeting the company's requirement for timely business decisions. While creating a separate schema (Option B) allows for direct querying, it introduces unnecessary complexity and maintenance overhead. Excluding the financial transaction data (Option C) would deprive the company of valuable insights. Data virtualization (Option D) might reduce storage costs but could negatively affect query performance, making it less suitable for real-time analytics.
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As a Microsoft Fabric Analytics Engineer Associate, you are tasked with integrating data from multiple sources into a single data warehouse for a financial services company. One of the sources is a financial transaction system that uses a different data format and schema than the other sources. The company requires real-time analytics on all data, including financial transactions, to make timely business decisions. Considering the need for data consistency, real-time access, and minimal maintenance overhead, which of the following approaches would you recommend for integrating the financial transaction data? (Choose one option.)
A
Transform the financial transaction data to match the data format and schema of the other sources before loading it into the data warehouse, ensuring data consistency and enabling real-time analytics.
B
Create a separate schema within the data warehouse to accommodate the unique data format and schema of the financial transaction system, allowing for direct querying but increasing maintenance complexity.
C
Exclude the financial transaction data from the data warehouse due to the differences in data format and schema, focusing on the remaining data for analytics.
D
Use a data virtualization approach to access the financial transaction data without physically loading it into the data warehouse, reducing storage costs but potentially impacting query performance.
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