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.

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.

In the event of a NameNode crash, the typical recovery procedure involves restoring from the Secondary NameNode. The Secondary NameNode contains a checkpoint of the metadata, allowing for a faster recovery compared to restarting the entire cluster.

The high-availability feature in YARN is achieved through ResourceManager Failover. This ensures continuous operation by having a standby ResourceManager ready to take over in case the primary ResourceManager fails, minimizing downtime and maintaining cluster availability.

The use of DataFrames in Apache Spark significantly enhances the speed of data transformations in a distributed environment. DataFrames provide a higher-level abstraction and optimization opportunities for Spark's Catalyst query engine, making it more efficient for processing large-scale data.