Data Compression Techniques in Cloud Data Warehouses Quiz

Explore essential concepts of data compression in cloud-based data warehouse platforms with this quiz. Assess your understanding of how compression techniques optimize storage, query performance, and resource utilization in modern database systems.

1. Compression Benefits

   Which of the following is a primary benefit of using data compression for columnar storage in large-scale data warehouses?

   1. Reduced storage footprint and improved query performance
   2. Increased disk write latency by introducing overhead
   3. Automatic data replication across multiple regions
   4. Enhanced network security by encrypting data

   Explanation: Data compression reduces the total storage required and often speeds up query processing because less data is read from disk. The other options are incorrect: compression does not inherently increase disk write latency, provide encryption, or manage data replication. Those are addressed by different technologies or configurations.

2. Selecting Compression Encoding

   When configuring a large fact table with mostly numeric and low-cardinality columns, which compression encoding is generally most effective?

   1. Lempel-Ziv Encoding
   2. Run-Length Encoding
   3. Dictionary Encoding
   4. Delta Encoding

   Explanation: Run-Length Encoding is ideal for columns where the same value appears consecutively, which is common with numeric, low-cardinality data. While Delta Encoding is used for sorted numeric data with incremental changes, Lempel-Ziv is better for unstructured text, and Dictionary Encoding suits high-cardinality text data. Thus, the other options are less effective here.

3. Impact on Query Performance

   How can enabling compression for large tables affect query performance in a columnar database system?

   1. It has no impact because data compression only saves disk space.
   2. It automatically caches all table data in memory.
   3. It can speed up queries by reducing the amount of data read from disk.
   4. It significantly slows down all queries due to constant decompression.

   Explanation: Compression often improves query speed as less physical data needs to be read, and decompression is typically fast relative to disk I/O. Option two is incorrect since decompressing data is typically optimized and not a major bottleneck. The third is incorrect as compression does affect query processing, and the fourth misrepresents the function of compression.

4. Compression Limitations

   Which type of column data is generally least likely to benefit significantly from compression algorithms like Dictionary Encoding?

   1. Columns using short text descriptions
   2. Columns containing boolean values
   3. Columns with repeated categorical tags
   4. Columns with high-cardinality unique identifiers

   Explanation: High-cardinality columns, where each value is usually unique, offer minimal compression benefit with Dictionary Encoding because there is little repetition to leverage. Repeated categorical tags and boolean values compress well due to redundancy. Although short text may compress, their benefit is still higher than unique identifiers.

5. Automatic Compression Selection

   What is the main advantage of using an automatic compression analysis tool during table creation?

   1. It generates database indexes automatically.
   2. It balances column data across multiple nodes equally.
   3. It evaluates sample data to recommend the most efficient compression method for each column.
   4. It encrypts the table to protect against unauthorized access.

   Explanation: Automatic compression analysis tools test sample data to identify the best compression scheme for each column, leading to optimal storage and performance. Encryption, indexing, and data distribution are distinct database functions unrelated to compression selection, and thus the other options are incorrect.