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 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.