PDE Ingesting and Processing the Data Practice Question
A company uses BigQuery to store event data. They need to load data from multiple sources with different schemas and expect frequent schema changes. Which approach provides the most flexibility for schema evolution while minimizing load failures and performance impact?
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 the Storage Write API in buffered mode with schema auto-detection
Using the Storage Write API with buffered mode allows schema auto-detection and flexible schema updates without failing loads, and provides better performance than legacy streaming inserts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Load data as JSON files in Cloud Storage and use external tables
Why it's wrong here
External tables have performance limitations and may not support all schema changes.
- ✓
Use the Storage Write API in buffered mode with schema auto-detection
Why this is correct
Buffered mode allows schema updates and auto-detection, reducing failures and handling schema evolution well.
- ✗
Use legacy streaming inserts with schema auto-detect enabled
Why it's wrong here
Legacy streaming inserts have lower throughput and may fail if schema changes are incompatible.
- ✗
Use Dataflow to preprocess and write to BigQuery using Storage Write API in committed mode
Why it's wrong here
Committed mode requires all columns to match, leading to failures on schema mismatch.
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