Ensuring efficient data processing in Hadoop involves focusing on input splitting during development. Input splitting is the process of dividing input data into manageable chunks, allowing parallel processing across nodes and optimizing job performance.

When a MapReduce job fails in Hadoop, the first step is typically to examine the logs. Hadoop generates detailed logs that provide information about the failure, helping developers identify the root cause and take corrective actions.

Snappy compression codec in Hadoop is known for providing a good balance between compression ratio and speed. It offers relatively fast compression and decompression while achieving a reasonable compression ratio, making it suitable for various use cases.

Avro is a common data serialization format in Hadoop used for efficient data transformation. It provides a compact binary format and is schema-aware, making it suitable for diverse data types and enabling efficient data processing in Hadoop ecosystems.

The Oozie SLA (Service Level Agreement) feature plays a crucial role in ensuring timely execution of workflows. It allows users to define performance expectations, and Oozie monitors and enforces these expectations, triggering alerts or actions if SLAs are not met.

A key difference between Avro and Parquet is how they handle data processing. Avro focuses on schema evolution, while Parquet excels in partitioning data. Parquet allows for efficient pruning and retrieval of specific data partitions, enhancing query performance.

In scenarios with iterative machine learning algorithms, Spark MLlib would be most beneficial. MLlib is Spark's machine learning library that provides high-level APIs for machine learning tasks, including iterative algorithms commonly used in machine learning workflows.

In Hadoop, the NameNode is responsible for storing metadata about files and directories in HDFS. It keeps track of the location and health of data blocks, playing a crucial role in the overall architecture of Hadoop's distributed file system.

When addressing 'Garbage Collection' issues in Java-based Hadoop applications, adjusting the Heap size parameter is a key strategy. Garbage Collection is the process of automatically reclaiming memory occupied by objects that are no longer in use, and adjusting the Heap size helps optimize memory management in Hadoop applications.

The advantage of using PySpark is simplified development. Python is known for its simplicity and readability, making it easier for developers to write and maintain code, resulting in increased productivity in comparison to the complexities of conventional MapReduce jobs.