In a scenario requiring the analysis of large datasets with minimal latency, Impala would be the preferable choice. Unlike Hive, which operates on a batch processing model, Impala provides low-latency SQL queries directly on the data stored in HDFS, making it suitable for real-time analytics.

The Comparator in MapReduce determines the order in which the output keys are sorted before they are passed to the Reducer. It plays a crucial role in arranging the intermediate key-value pairs for effective data processing in the Reducer phase.

Checksums are used during data loading in Hadoop to ensure data integrity across the cluster. Hadoop calculates and verifies checksums for each data block, identifying and handling data corruption issues to maintain the reliability of stored data.

YARN enables dynamic resource allocation, allowing it to allocate resources efficiently between batch and real-time processing applications. This flexibility ensures that the cluster can adapt to varying workloads and allocate resources based on the specific needs of each application.

Apache HBase enhances Hadoop's capabilities by providing real-time access to Big Data. Unlike HDFS, which is optimized for batch processing, HBase supports random read and write operations, making it suitable for real-time applications and scenarios requiring low-latency data access.

Python is commonly used for scripting Hadoop jobs due to its simplicity and ease of use. It provides a high-level scripting interface, making it convenient for writing Hadoop jobs, especially for tasks like data processing and analysis.

A common issue in Hadoop clusters, where one node is overloaded while others are underutilized, is known as Load Balancing. Load balancing ensures that the computational workload is evenly distributed across all nodes, optimizing resource utilization and preventing performance bottlenecks.

The ResourceManager in YARN is responsible for monitoring the resource usage in a node and managing the user's job execution. It keeps track of available resources and allocates them to applications.

In Hadoop development, the principle of Data Locality is essential for managing large-scale data processing. Data Locality ensures that data is processed on the same node where it is stored, reducing data transfer overhead and enhancing the efficiency of data processing in Hadoop.

The primary role of the Mapper in the MapReduce framework is data transformation. Mappers take input data and convert it into key-value pairs, which are then processed by the subsequent stages of the MapReduce job. This phase is crucial for dividing the workload and preparing the data for further analysis.