This is a regular “data quiz”. Follow it on LinkedIn. Test your knowledge or learn something new.
Today Question:
Which principle reduces costs in big data processing?
A) Denormalization
B) Partitioning
C) Sharding
D) All of the above
Correct Answer: D
Explanation
All three principles—denormalization, partitioning, and sharding—are effective strategies for reducing costs in big data processing. DENORMALIZATION reduces the number of JOIN operations by combining data from multiple tables into one place, improving analytical query performance and lowering compute resource usage. PARTITIONING splits large tables into smaller segments based on a key such as date, which allows queries to scan only relevant partitions and reduces IO and processing time. SHARDING distributes data across multiple nodes or databases, enabling parallel processing and horizontal scaling instead of expensive vertical scaling. Combining these approaches creates a synergistic effect. For example, partitioned tables can also be sharded and denormalized, leading to optimal performance at the lowest possible infrastructure and compute cost.