In the case of frequent data processing slowdowns, prioritizing Hadoop performance monitoring using tools like Ambari is crucial. Ambari provides a comprehensive view of cluster health, performance metrics, and allows for efficient management and troubleshooting to identify and address performance bottlenecks.

Advanced MapReduce jobs often require Apache Spark to manage complex data dependencies and transformations. Apache Spark provides in-memory processing and a rich set of APIs, making it suitable for iterative algorithms, machine learning, and advanced analytics on large datasets.

Hadoop ensures data durability through data replication. Each data block is replicated across multiple nodes in the cluster, and in the event of a single node failure, the data can still be accessed from the replicated copies, ensuring fault tolerance and data availability.

HiveQL in Apache Hive resembles SQL (Structured Query Language) most closely. It is designed to provide a familiar querying interface for users who are already familiar with SQL syntax. This makes it easier for SQL developers to transition to working with big data using Hive.

HiveQL allows users to write custom mappers and reducers using the TRANSFORM clause. This clause enables the integration of external scripts, such as those written in Python or Perl, to process data in a customized way within the Hive framework.

Python's integration with Hadoop is enhanced by the PySpark library, which provides a Python API for Apache Spark. PySpark enables efficient data processing, machine learning, and analytics, making it a popular choice for Python developers working with Hadoop.

Sqoop's encrypted mode is used to secure sensitive data during transfer. By enabling encryption, Sqoop ensures that the data being transferred between systems is protected and secure, addressing concerns related to data confidentiality during the import/export process.

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.

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.