In Hadoop, the process of adding more nodes to a cluster is known as Scaling Out. This involves increasing the number of nodes in the cluster to handle growing data volumes and enhance processing capabilities. Scaling out is a key strategy to accommodate the scalability requirements of big data applications.

In Hadoop Streaming, the InputFormat serves as a connector between the script and the Hadoop framework. It defines how the data is read and presented to the mapper for processing. The InputFormat specifies the input data's structure and how it should be split and processed.

Parquet is a column-oriented file format in Hadoop designed for optimal query performance on large datasets. It organizes data in a columnar fashion, allowing for efficient compression and improved read performance, making it suitable for analytical workloads.

The 'Reduce' method in the Reducer class is essential for aggregating the outputs generated by the Mapper tasks. It processes the intermediate key-value pairs, performs the required operations, and produces the final result of the MapReduce job.

Data Compression is an essential step in data loading to optimize the storage and processing of large datasets in Hadoop. Compression reduces the storage space required for data and speeds up data transfer, improving overall performance in Hadoop clusters.

The heartbeat status is crucial for assessing the health of a DataNode in a Hadoop cluster. DataNodes send periodic heartbeats to the NameNode to confirm their availability. If the NameNode stops receiving heartbeats from a DataNode, it may be an indication of a node failure or network issues.

Kerberos plays a crucial role in Hadoop cluster security, providing strong authentication and authorization mechanisms. It ensures that only authorized users and processes can access Hadoop resources, enhancing the overall security of the cluster.

Apache Tez is used in the Hadoop ecosystem to enhance batch processing efficiency through resource optimization. It provides a more efficient execution engine for processing complex data processing tasks.

For complex iterative algorithms, Resilient Distributed Datasets (RDDs) in Apache Spark offer a significant advantage. RDDs provide fault tolerance and in-memory processing, reducing the need for repetitive data loading and enabling iterative algorithms to operate more efficiently.

Apache Pig optimizes execution plans through Lazy Evaluation. It delays the execution of operations until the last possible moment, allowing Pig to generate a more efficient execution plan based on the actual data flow and reducing unnecessary computations.

Crunch's Lazy Evaluation mechanism is designed to optimize the execution of MapReduce jobs in Hadoop. It delays the execution of certain operations until necessary, reducing redundant computations and improving performance.

The most effective combination for real-time log file ingestion and analysis in Hadoop is Kafka for data streaming and Spark Streaming for real-time data processing. Kafka provides high-throughput, fault-tolerant, and scalable data streaming, while Spark Streaming allows processing and analyzing data in near-real-time.