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.

Kafka ensures data integrity during transfer through its Idempotence feature. This feature guarantees that messages are processed exactly once, preventing duplicates and maintaining data consistency in a distributed environment.

When dealing with unstructured data in Hadoop applications, flexibility should be given the highest priority. Unstructured data often lacks a predefined schema, and Hadoop frameworks like HDFS and MapReduce can handle diverse data formats, allowing for flexible processing and analysis.

Apache Hive is a Hadoop-based data warehousing tool that facilitates the writing and execution of SQL-like queries, known as HiveQL, for data transformation and analysis. It translates these queries into MapReduce jobs for efficient processing.

The 'FOREACH ... GENERATE' statement in Apache Pig is used for data transformation. It allows users to apply transformations to individual fields or create new fields based on existing ones, enabling the extraction and modification of data as needed.

When developing a real-time analytics application in Scala on Hadoop, integrating Apache Spark with Apache Kafka ensures optimal performance. Spark provides real-time processing capabilities, and Kafka facilitates efficient and scalable data streaming.

Apache Oozie workflows are typically defined using XML (eXtensible Markup Language). XML provides a structured and standardized way to represent the workflow configuration, making it easier for users to define and understand the workflow structure.

The Snappy compression codec is known for faster compression and decompression speeds compared to Gzip. While Gzip offers a higher compression ratio, Snappy excels in scenarios where speed is a priority, making it suitable for certain Hadoop use cases where rapid data processing is essential.

Optimizing Hadoop's garbage collection can reduce latency by minimizing the time spent on memory management. It ensures efficient memory usage, preventing long pauses and improving overall cluster performance.