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.

In a Hadoop cluster, the YARN (Yet Another Resource Negotiator) tool is used for cluster resource management and job scheduling. YARN separates resource management and job scheduling functionalities in Hadoop, allowing for more efficient cluster utilization.

For time-series data storage, configuring HBase with Time-to-Live (TTL) can be advantageous. TTL allows you to automatically expire data after a specified period, which is useful for managing and cleaning up older time-series data, optimizing storage, and improving query performance.

To handle large-scale data processing, Hadoop clusters are often scaled 'Horizontally.' This means adding more commodity hardware or nodes to the existing cluster, allowing it to distribute the workload and handle increased data processing demands.

The process of ETL (Extract, Transform, Load) is crucial for transferring bulk data between Hadoop and external data sources. ETL involves extracting data from external sources, transforming it into a suitable format, and loading it into the Hadoop cluster for analysis.

Apache Pig scripts are primarily written in Pig Latin, a high-level scripting language designed for expressing data analysis programs in a concise and readable way. Pig Latin scripts are then translated into MapReduce jobs for execution on a Hadoop cluster.