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.

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.

One strategy to optimize a Reduce task that is slower than the Map tasks is Speculative Execution. In this approach, multiple instances of the same Reduce task are launched on different nodes, and the one that finishes first is accepted, reducing the overall job completion time.

The hdfs-site.xml file in Hadoop configuration specifies the number of replicas for each block in HDFS. This configuration is essential for ensuring fault tolerance and data reliability by controlling the replication factor of data blocks across the cluster.

When a Hadoop job is running slower than expected, the initial check should focus on Hadoop configuration. This includes parameters related to memory, task allocation, and parallelism. Suboptimal configuration settings can significantly impact job performance.

A local job runner in Hadoop unit testing simulates the Hadoop environment locally. It allows developers to test their MapReduce jobs on a single machine before deploying them on a Hadoop cluster, facilitating faster development cycles and easier debugging.