Google Cloud Professional Data Engineer Practice Exam 2025 – Complete Preparation

Data sharding refers to dividing a dataset into smaller, more manageable pieces, which allows for improved performance and easier data management. This technique is particularly useful for distributed databases, as it enables the system to spread the load across multiple machines instead of having a single database instance handle all requests. Sharding can enhance query performance and ensure that the database can scale effectively as the volume of data grows.

In practical applications, each shard can be processed in parallel, leading to faster access times for users and applications. Furthermore, if a particular shard becomes too large or if the load on it increases significantly, it can be further divided into smaller shards, maintaining efficient performance as data scales.

In the context of the other options, compressing a dataset pertains to reducing the size of the data rather than managing its structure. Transforming raw data into structured data is about data processing and organization but does not involve dividing datasets. Aggregating data from multiple sources is focused on combining data rather than managing it in smaller parts. Thus, the essence of data sharding revolves around dividing to optimize for performance and manageability.