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.

Parquet enhances query performance through Predicate Pushdown. This strategy involves pushing parts of the query execution directly to the storage layer, reducing the amount of data that needs to be processed by the query engine. This is particularly effective for columnar data storage, like Parquet, where only relevant columns are read during query execution.

For enhanced security in a Hadoop cluster dealing with sensitive information, prioritizing the security of the NameNode is crucial. The NameNode contains metadata and information about data locations, making it a potential target for security threats. Securing the NameNode helps safeguard sensitive data in the cluster.

Advanced debugging in Hadoop often involves analyzing Log Files to diagnose issues in job execution. Log files contain valuable information about the steps taken during job execution, helping developers identify and resolve issues in the Hadoop application.

For encrypting sensitive data at rest and in transit, Hadoop Transparent Data Encryption (TDE) is a crucial security feature. TDE encrypts data stored in HDFS, adding an extra layer of protection, and ensures that data transferred between nodes is encrypted, safeguarding it from unauthorized access.

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.

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.