Data Velocity is an essential factor in determining the choice between batch and real-time processing in Hadoop applications. It refers to the speed at which data is generated and the need for processing it in real-time or in batches based on the application requirements.

Hadoop operates on the principle of "Data parallelism," which enables it to process large datasets by dividing the workload into smaller tasks that can be executed in parallel on multiple nodes.

Advanced cluster monitoring in Hadoop involves analyzing log files for predictive maintenance and optimization. Log files contain valuable information about the cluster's performance, errors, and resource utilization, helping administrators identify and address issues proactively.

In Hadoop, YARN (Yet Another Resource Negotiator) plays a critical role in scheduling and coordinating workflow execution in data pipelines. YARN manages resources efficiently, enabling multiple applications to share and utilize resources on a Hadoop cluster.

In Apache Oozie, Decision actions allow conditional control flow in workflows. They enable the workflow to take different paths based on the outcome of a condition, providing flexibility in designing complex workflows.

In a Hadoop cluster, the NameNode acts as the master. It manages the metadata and keeps track of the location of data blocks in the Hadoop Distributed File System (HDFS). The NameNode is a critical component for ensuring data integrity and availability.

You would leverage Avro for storage and Parquet for querying in a Hadoop environment with high query performance requirements. Avro's fast serialization is suitable for write-heavy workloads, while Parquet's columnar storage format enhances query performance by reading only the required columns.

In a scenario with seasonal spikes, auto-scaling is crucial in capacity planning. Auto-scaling allows the cluster to dynamically adjust resources based on demand, ensuring optimal performance during peak periods without unnecessary over-provisioning during off-peak times.

HiveQL queries are translated into MapReduce jobs by Hive. MapReduce is the underlying processing framework that Hive uses to execute queries on large datasets stored in Hadoop Distributed File System (HDFS).

In Hadoop, Failover mechanisms are implemented to automatically recover from a node or service failure. These mechanisms ensure the seamless transition of tasks and services to healthy nodes in the event of a failure, enhancing the overall system's resilience.