Apache Ranger is typically used to implement role-based access control (RBAC) in Hadoop. It provides a centralized framework for managing and enforcing fine-grained access policies, allowing administrators to define roles and permissions for Hadoop components.

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.

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.

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.

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.