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.

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 Hive, Predicate Pushdown is a mechanism that enables more efficient data retrieval by pushing filtering conditions closer to the data source. It helps to skip over irrelevant data early in the query execution process, improving performance.

When planning the capacity of a Hadoop cluster, network bandwidth is a critical metric for balancing the load across DataNodes. It ensures efficient data transfer and prevents bottlenecks in the network, optimizing the overall performance of the cluster.

Advanced Sqoop integrations often involve Apache Hive for optimized data transfers and transformations. Hive provides a data warehousing infrastructure on top of Hadoop, allowing for SQL-like queries and efficient data processing.

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.

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.

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.

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 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.