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As a Microsoft Fabric Analytics Engineer Associate, you are designing a data pipeline to ingest real-time streaming data into a lakehouse. The data sources include IoT sensor data, social media feeds, and financial transaction data, each with high volume and velocity. The solution must ensure low latency for financial transactions, scalability to handle IoT data spikes, and compliance with data privacy regulations for social media feeds. Considering these requirements, which approach would you choose to design the pipeline? (Choose one option)
A
Implement a batch processing system to collect and process data at scheduled intervals, ensuring all data is processed uniformly.
B
Deploy a stream processing system that processes data in real-time, enabling immediate analysis and action for time-sensitive data like financial transactions.
C
Combine batch and stream processing systems, using batch for historical data analysis and stream for real-time data, to cover all data processing needs.
D
Utilize a distributed processing system to parallelize data ingestion and processing, focusing on throughput rather than latency.