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 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.

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.

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.

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.

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.

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.

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.

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.

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.

In a sudden performance degradation scenario, the primary focus should be on memory usage. High memory consumption can lead to performance issues as it affects task execution and overall cluster efficiency. Analyzing memory usage can help identify resource bottlenecks and optimize performance.

Advanced use of Hadoop Streaming API involves the implementation of MapReduce for efficient data sorting and aggregation. MapReduce is a key processing model in Hadoop, and integrating it with Streaming API allows for complex data processing tasks, including sorting and aggregation, in a distributed fashion.