PDE Designing Data Processing Systems Practice Question
Your company is building a real-time anomaly detection system for financial transactions. The system must process streams of transactions and flag anomalies within seconds. The volume is moderate (5000 transactions per second). You want a fully managed solution that integrates with BigQuery for historical analysis. Which service should you use for stream processing?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Cloud Dataflow
Cloud Dataflow is a fully managed service ideal for real-time stream processing with low latency. It can read from Pub/Sub, perform transformations (e.g., anomaly detection), and write to BigQuery for historical analysis. Dataproc requires cluster management; Data Fusion is batch-oriented; Pub/Sub alone does not process data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Cloud Dataflow
Why this is correct
Dataflow is fully managed, handles streaming with sub-second latency, and integrates natively with Pub/Sub and BigQuery.
- ✗
Cloud Pub/Sub with push subscriptions
Why it's wrong here
Pub/Sub alone does not process data; it only delivers messages.
- ✗
Cloud Dataproc with Spark Streaming
Why it's wrong here
Dataproc requires manual cluster management and scaling, increasing operational overhead for moderate throughput.
- ✗
Cloud Data Fusion
Why it's wrong here
Data Fusion is primarily batch ETL, not real-time stream processing.
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