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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.