DP-203 Develop data processing Practice Question
You are designing a data pipeline in Azure Synapse Analytics to ingest data from Azure Blob Storage into a dedicated SQL pool. The source files are CSV with varying row lengths, and you need to ensure optimal performance for reads. Which file format and compression should you recommend?
⚠ Common exam trap
Microsoft often tests the misconception that row-based formats like Avro or CSV are suitable for analytical workloads, but the trap here is that columnar formats (Parquet/ORC) are required for optimal read performance in Synapse dedicated SQL pools, and Snappy is preferred over Zlib for speed-critical pipelines.
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
✓
Parquet with Snappy compression
Parquet with Snappy compression is optimal for dedicated SQL pools in Azure Synapse Analytics because Parquet is a columnar format that enables efficient predicate pushdown and column pruning, reducing I/O. Snappy provides fast compression/decompression with minimal CPU overhead, which is critical for high-throughput reads in a distributed MPP environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Avro with Deflate compression
Why it's wrong here
Avro is row-oriented, not optimal for analytical queries.
- ✗
CSV with Gzip compression
Why it's wrong here
CSV is not columnar, leading to higher I/O and slower performance.
- ✓
Parquet with Snappy compression
Why this is correct
Parquet is columnar and Snappy provides fast compression/decompression, ideal for Synapse dedicated SQL pool.
- ✗
ORC with Zlib compression
Why it's wrong here
ORC is not natively supported in Synapse dedicated SQL pools.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
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