Sqoop's Incremental Load feature enables the efficient transfer of only new or updated data from a database to Hadoop. This helps in minimizing data transfer time and resources when dealing with large datasets.

Visualization tools are commonly used for visualizing Hadoop cluster metrics and logs. These tools provide insights into the performance and health of the Hadoop cluster, helping administrators identify issues and optimize performance.

Apache Pig is utilized for complex data transformation and analysis in Hadoop. It allows users to write scripts using a high-level scripting language called Pig Latin, making it easier to process and analyze large datasets.

The best approach for ingesting and analyzing log data from multiple sources in a Hadoop pipeline is to use Apache Flume. Flume is designed for efficient, reliable, and scalable data ingestion, making it suitable for handling log data streams.

In Hadoop, Scalability is a critical aspect to test when dealing with large-scale data processing. It refers to the system's ability to handle increasing amounts of data and workloads effectively, ensuring that it can scale horizontally to accommodate growing datasets.

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.

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.