PDE Designing Data Processing Systems Practice Question
You are designing a batch data pipeline that runs daily to ingest data from an on-premises database into BigQuery. The ingestion volume is approximately 50 GB per day. The data must be available in BigQuery by 6 AM each day. The on-premises database supports change data capture (CDC) via logs. Which approach minimizes operational cost and complexity?
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
✓
Use Cloud Dataflow with a JDBC source in batch mode to read CDC logs and write to BigQuery
Using Dataflow with a JDBC source to read CDC logs in batch mode is straightforward and cost-effective for daily 50 GB loads. Dataproc could also work but requires cluster management. Pub/Sub with Dataflow would be more complex and costly for a daily batch. Data Fusion adds a visual layer but is overkill for this simple batch ingestion.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Cloud Dataproc with Spark Streaming to ingest CDC logs
Why it's wrong here
Spark Streaming is overkill for a daily batch job; it adds complexity and cluster management overhead.
- ✗
Use Pub/Sub with a Dataflow streaming pipeline
Why it's wrong here
Streaming is unnecessary for a daily batch; it increases cost and complexity.
- ✗
Use Cloud Data Fusion with a batch pipeline
Why it's wrong here
Data Fusion is a visual ETL tool that adds overhead; not the simplest option for this straightforward ingestion.
- ✓
Use Cloud Dataflow with a JDBC source in batch mode to read CDC logs and write to BigQuery
Why this is correct
Dataflow can read from JDBC in batch mode, handle CDC, and write to BigQuery. It is fully managed and cost-effective for this volume.
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