In a Hadoop-based ETL process, choosing ORC (Optimized Row Columnar) file format with Gzip compression is ideal for optimized data transfer. ORC provides efficient storage and Gzip offers a good balance between compression ratio and speed.

In a Hadoop cluster, the YARN (Yet Another Resource Negotiator) tool is used for cluster resource management and job scheduling. YARN separates resource management and job scheduling functionalities in Hadoop, allowing for more efficient cluster utilization.

For time-series data storage, configuring HBase with Time-to-Live (TTL) can be advantageous. TTL allows you to automatically expire data after a specified period, which is useful for managing and cleaning up older time-series data, optimizing storage, and improving query performance.

To handle large-scale data processing, Hadoop clusters are often scaled 'Horizontally.' This means adding more commodity hardware or nodes to the existing cluster, allowing it to distribute the workload and handle increased data processing demands.

In scenarios with complex data transformations, the MultiQuery Optimization feature of Apache Pig would be most efficient. This feature allows multiple Pig Latin queries to be executed together, optimizing the execution plan and improving overall performance in situations with intricate data transformations.

The integration of Apache Storm with Hadoop allows for advanced real-time analytics on large data streams. Storm is a distributed stream processing framework that can process high-velocity data in real-time, making it suitable for applications requiring low-latency processing.

In the context of Big Data, Velocity refers to the rapid speed at which data is generated, collected, and processed. It highlights the high frequency and pace of data flow in modern data-driven environments.

To achieve scalability beyond thousands of nodes, YARN introduced a ResourceManager that manages the cluster's resources. The ResourceManager is responsible for resource allocation and management across the entire Hadoop cluster.

Impala achieves faster query performance compared to Hive by utilizing in-memory processing. Unlike Hive, which relies on MapReduce and disk-based processing, Impala keeps frequently accessed data in memory, reducing query latency and improving overall performance.

For large-scale Hadoop deployments, predictive analytics is crucial for proactive cluster health and performance management. Predictive analytics leverages historical data and machine learning models to predict potential issues, allowing administrators to take preventive measures and optimize the cluster's overall performance.