AWS Certified Data Engineer – AssociateData Storage and ManagementMedium
A data engineer is working with a large dataset (hundreds of terabytes) stored in Amazon S3, consisting of millions of small CSV files. This data is frequently queried by Amazon Athena, leading to high query costs and slow performance. To optimize this, the engineer decides to convert the data to Parquet format and implement a compaction strategy. Which compression codec, when combined with Parquet, would offer the best balance of compression ratio and query performance for columnar analytical queries?
- ALZMA
- BGZIP
- CSnappy
- DBZIP2
Show answer & explanationAnswer & explanation
Correct answer: C. Snappy
Snappy offers a good balance of high compression ratio and fast decompression, which is crucial for query performance in columnar formats like Parquet, making it ideal for analytical workloads with Athena.
Why the other options are wrong
- A. LZMA (used in XZ) offers excellent compression but very slow decompression, making it unsuitable for analytical query performance.
- B. GZIP offers a high compression ratio but slower decompression, potentially impacting query performance more than Snappy.
- D. BZIP2 provides a very high compression ratio but is significantly slower for both compression and decompression, hindering query performance.
Snappy Compression
Snappy is a compression algorithm developed by Google that prioritizes high speed and reasonable compression ratios, making it well-suited for columnar data formats like Parquet where fast decompression is critical for query performance.
- Optimized for speed (compression/decompression)
- Good compression ratio, not highest
- Commonly used with Parquet and ORC
- Reduces I/O and improves query performance
Memory trick: Snappy's speed makes data query quickly.