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As a data engineer responsible for designing a data architecture on Microsoft Azure that leverages Delta Lake for streaming data ingestion, you are tasked with ensuring the system can handle high-velocity data while maintaining data quality and query performance. Consider the following scenario: Your organization is processing millions of events per second from IoT devices, and the data must be available for real-time analytics with strict compliance and cost constraints. Which of the following approaches BEST addresses these requirements by leveraging Delta Lake's capabilities? Choose one option.
As a data engineer responsible for designing a data architecture on Microsoft Azure that leverages Delta Lake for streaming data ingestion, you are tasked with ensuring the system can handle high-velocity data while maintaining data quality and query performance. Consider the following scenario: Your organization is processing millions of events per second from IoT devices, and the data must be available for real-time analytics with strict compliance and cost constraints. Which of the following approaches BEST addresses these requirements by leveraging Delta Lake's capabilities? Choose one option.
Explanation:
The correct answer is C because it effectively leverages Delta Lake's micro-batch processing and ACID transactional capabilities to handle high-velocity data ingestion while ensuring real-time data quality checks and maintaining query performance. This approach meets the requirements for handling millions of events per second, supports real-time analytics, and adheres to compliance and cost constraints. Option A neglects data quality and query performance, which are critical for real-time analytics. Option B introduces unnecessary latency and complexity by separating data quality checks from the ingestion pipeline. Option D sacrifices essential Delta Lake features that are crucial for data integrity and performance.