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.

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.

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.

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.

For troubleshooting intermittent failures in a Hadoop application, a crucial strategy is Log Analysis. Examining logs provides insights into error messages, stack traces, and events leading to failure, helping diagnose and address issues effectively.

Load balancing across DataNodes is essential to maintain optimal performance in a Hadoop cluster. Load balancing ensures that the processing workload is evenly distributed among the nodes, preventing resource bottlenecks and maximizing the efficiency of the entire cluster.

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.