To achieve scalability beyond thousands of nodes, YARN introduced a ResourceManager that manages the cluster's resources. The ResourceManager is responsible for resource allocation and management across the entire Hadoop cluster.

Impala achieves faster query performance compared to Hive by utilizing in-memory processing. Unlike Hive, which relies on MapReduce and disk-based processing, Impala keeps frequently accessed data in memory, reducing query latency and improving overall performance.

For large-scale Hadoop deployments, predictive analytics is crucial for proactive cluster health and performance management. Predictive analytics leverages historical data and machine learning models to predict potential issues, allowing administrators to take preventive measures and optimize the cluster's overall performance.

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.

Hive is the component in the Hadoop ecosystem primarily used for data warehousing and SQL queries. It provides a high-level language, HiveQL, for querying data stored in Hadoop's distributed storage, making it accessible to analysts familiar with SQL.

In scenarios involving the joining of large datasets, the optimization features of Apache Pig, such as query optimization and parallel execution, significantly improve data processing efficiency. These optimization techniques help in handling large-scale data transformations more effectively, ensuring better performance in complex processing tasks.