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.

Garbage Collection is a recommended practice in Hadoop for efficient memory management. It involves automatic memory management by identifying and freeing up memory occupied by objects that are no longer in use. Proper garbage collection enhances the performance of Hadoop applications.

For complex data processing tasks in a Hadoop environment using Python, PySpark is the most suitable library. PySpark provides a Python API for Apache Spark, allowing data scientists to leverage the power of Spark for distributed and parallel processing of large datasets.

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.

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.

Apache Pig uses a dynamic schema approach in data processing. This means that Pig doesn't enforce a rigid schema on the data; instead, it adapts to the structure of the data at runtime. This flexibility allows Pig to handle semi-structured or unstructured data effectively.

The 'V' that refers to the trustworthiness and reliability of data in the context of Big Data is Veracity. It emphasizes the quality and accuracy of the data, ensuring that the information is reliable and trustworthy for making informed decisions.