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.

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.

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 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.