In case of frequent DataNode failures, a key troubleshooting step is to inspect DataNode logs. These logs provide insights into the issues causing failures, such as disk errors or communication problems. Analyzing logs helps in identifying and addressing the root cause of the problem.

Hadoop's YARN (Yet Another Resource Negotiator) framework enhances resource management by introducing dynamic resource allocation. Unlike classic MapReduce, YARN allows applications to request and use resources dynamically, optimizing resource utilization and making the cluster more flexible and efficient.

Speculative execution in Hadoop allows the framework to launch multiple instances of the same task on different nodes. If one instance finishes earlier, the results are used, improving parallelism and overall job performance.

In the context of cluster optimization, Snappy compression reduces storage needs and speeds up data transfer in HDFS. Snappy is a fast compression algorithm that strikes a balance between compression ratio and decompression speed, making it suitable for Hadoop environments.

Hadoop handles the unavailability of multiple DataNodes by replicating data across the cluster. Data Replication ensures data durability and fault tolerance, allowing the system to recover from node failures.

Apache Spark is a popular framework in Hadoop used for real-time processing and analytics of streaming data. It provides in-memory processing capabilities, making it suitable for iterative algorithms and interactive data analysis.

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.

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.

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.

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.