Clustering and partition difference in data warehousing solutions

Question

Accepted Answer

Clustering involves grouping similar data together, while partitioning involves dividing data into smaller, manageable sections.

Clustering is used to group similar data points together based on certain criteria, such as customer segments or product categories.
Partitioning involves dividing a large dataset into smaller, more manageable sections for easier data retrieval and processing.
Clustering is often used for data analysis and machine learning tasks, while partitioning is commonly used in data warehousing solutions for performance optimization.
Examples of clustering algorithms include K-means and hierarchical clustering, while partitioning can be done based on ranges of values or hash functions.

Top Verizon Data Engineer interview questions & answers