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.

In a Hadoop-based ETL process, choosing ORC (Optimized Row Columnar) file format with Gzip compression is ideal for optimized data transfer. ORC provides efficient storage and Gzip offers a good balance between compression ratio and speed.

In Crunch, a PCollection is used to represent a distributed dataset in Hadoop. It is a parallel collection of data, and Crunch provides a high-level Java API for building data processing pipelines.

A key consideration for integrating Kerberos in a Hadoop cluster is achieving Single Sign-On (SSO). Kerberos provides a centralized authentication system, allowing users to log in once and access various services without the need to re-enter credentials. This enhances security and simplifies user access management.

The configuration parameter that sets the default replication factor for data blocks in HDFS is dfs.replication. It determines the number of copies that Hadoop creates for each data block to ensure fault tolerance and data durability.

In scenarios with complex data transformations, the MultiQuery Optimization feature of Apache Pig would be most efficient. This feature allows multiple Pig Latin queries to be executed together, optimizing the execution plan and improving overall performance in situations with intricate data transformations.

The integration of Apache Storm with Hadoop allows for advanced real-time analytics on large data streams. Storm is a distributed stream processing framework that can process high-velocity data in real-time, making it suitable for applications requiring low-latency processing.

In the context of Big Data, Velocity refers to the rapid speed at which data is generated, collected, and processed. It highlights the high frequency and pace of data flow in modern data-driven environments.