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Data Partitioning

Dividing data into smaller parts for parallel processing

Data Partitioning is the process of dividing large datasets into smaller parts or partitions for parallel processing and better performance in distributed computing environments.

5 minutes 5 Questions

Data Partitioning is a fundamental technique in Big Data engineering that involves dividing large datasets into smaller, more manageable segments called partitions. Each partition contains a subset of the data based on specific criteria such as date ranges, geographic regions, categorical values, o…

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Big Data Engineer - Data Partitioning Example Questions

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Question 1

What is the main disadvantage of round-robin partitioning?

Inability to handle large data sizes. Uneven data distribution among partitions. Difficulty in implementing parallel processing. Low query performance. High memory overhead. Lack of fault tolerance.

Correct Answer: Uneven data distribution among partitions.

Round-robin partitioning involves distributing data sequentially across partitions, which can lead to uneven data distribution and skewed data partitions. This can negatively impact query performance and create a bottleneck in the partition with the most data.

Question 2

What is a key-value store?

A database that stores data in document structures. A database that stores data in tables and rows. A database that stores data as key-value pairs. A database that stores data in graph structures. A database that stores data in XML or JSON formats. A database that stores data in hierarchical structures.

Correct Answer: A database that stores data as key-value pairs.

A key-value store is a type of database that stores data as key-value pairs. This allows for very fast retrieval of data, making it useful for applications with high read and write loads. Key-value stores are often used for caching, session management, and other caching-intensive applications.

Question 3

What is the difference between vertical and horizontal partitioning in data partitioning?

Vertical partitioning is more efficient for small datasets, while horizontal partitioning is more efficient for large datasets. Horizontal partitioning is more efficient for small datasets, while vertical partitioning is more efficient for large datasets. Vertical partitioning splits data across columns or attributes, while horizontal partitioning splits data across rows. Vertical partitioning splits data across rows, while horizontal partitioning splits data across columns or attributes. Horizontal partitioning is only suitable for structured data, while vertical partitioning is suitable for both structured and unstructured data. Vertical partitioning is only suitable for structured data, while horizontal partitioning is suitable for both structured and unstructured data.

Correct Answer: Vertical partitioning splits data across columns or attributes, while horizontal partitioning splits data across rows.

Vertical partitioning is performed based on the columns or attributes of the dataset, while horizontal partitioning is performed based on the rows of the dataset. The choice between these techniques depends on the nature of the dataset and the querying requirements.

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