AWS Certified Data Engineer – AssociateData Storage and ManagementHard
A data engineer is designing a data lake using Amazon S3. The data consists of large files (hundreds of MBs to several GBs each) that are frequently accessed by analytical queries. The team wants to reduce storage costs and improve query performance by minimizing I/O operations. Which compression format should the data engineer recommend for these files, considering both compression ratio and query performance?
- ABZIP2
- BSnappy
- CGZIP
- DLZ4
Show answer & explanationAnswer & explanation
Correct answer: B. Snappy
Snappy is a compression format optimized for speed rather than maximum compression. While it might not achieve the highest compression ratio, it offers a good balance between file size reduction and decompression speed, which is crucial for query performance in data lake scenarios where data is frequently scanned. It's often preferred for analytical workloads over GZIP or BZIP2 due to faster decompression.
Why the other options are wrong
- A. BZIP2 provides the highest compression ratio but is significantly slower to compress and decompress, making it generally unsuitable for performance-sensitive analytical queries.
- C. GZIP offers a good compression ratio but is slower to decompress than Snappy, which can impact query performance, especially in analytical workloads.
- D. LZ4 is even faster than Snappy but often achieves a slightly lower compression ratio. Snappy is generally a more common and well-balanced choice for general data lake analytical workloads.
Snappy Compression
A fast compression/decompression algorithm optimized for speed rather than maximum compression, commonly used in big data ecosystems for analytical workloads.
- Optimized for speed (fast compression/decompression)
- Good balance of compression ratio and performance
- Widely used in Hadoop, Spark, and data lakes
- Improves query performance by reducing I/O
Memory trick: Snappy is 'Snappy' fast for your analytical queries.