Python is commonly used for writing scripts that can be processed by Hadoop Streaming. The flexibility of Hadoop Streaming allows the use of scripting languages, and Python is a popular choice for its simplicity and readability.

Profiling is a technique used in complex Hadoop applications to identify and isolate performance bottlenecks. It involves analyzing the execution of the code to understand resource utilization, execution time, and memory usage, helping developers optimize performance-critical sections.

Hadoop employs the strategy of data replication to balance load and ensure data availability across the cluster. Data is replicated across multiple nodes, providing fault tolerance and enabling parallel processing by allowing tasks to be executed on the closest available data copy.

In Hadoop, the NetworkTopology is vital for monitoring and managing network traffic and data flow. It represents the physical network structure, helping optimize data transfer by placing computation closer to the data source.

The primary challenge in unit testing Hadoop applications involving HDFS is handling large datasets. Unit testing typically involves smaller datasets, and dealing with the volume of data in HDFS during testing poses challenges. Strategies like using smaller datasets or mocking HDFS interactions are often employed to address this challenge.

MapReduce Combiner is an optimization technique that allows for efficient data aggregation before sending data to the reducers. It helps reduce the amount of data shuffled across the network, improving overall performance in MapReduce jobs.

The component of Apache Pig that translates scripts into MapReduce jobs is the Pig Compiler. It takes Pig Latin scripts as input and converts them into a series of MapReduce jobs that can be executed on a Hadoop cluster for data processing.

Apache Spark's ClusterManager feature allows for dynamic allocation of resources based on workload. The ClusterManager dynamically adjusts the resources allocated to Spark applications based on their needs, optimizing resource utilization.

Resource management is a key aspect of managing and optimizing cluster performance in Hadoop. Tools like YARN (Yet Another Resource Negotiator) play a crucial role in efficiently allocating and managing resources for running applications in the Hadoop cluster.

HBase is a distributed NoSQL database that integrates with the Hadoop ecosystem for efficient data storage and retrieval. It is designed to handle large volumes of sparse data and is well-suited for random, real-time read/write access to Hadoop data.