The ResourceManager in YARN architecture is responsible for allocating system resources to different applications running on the Hadoop cluster. It keeps track of available resources and schedules tasks based on the requirements of the applications.

The format of input data is crucial in Hadoop application development as it directly impacts job performance. Choosing the right input format, such as Hadoop's preferred formats like SequenceFile or Avro, can enhance data processing efficiency.

Compaction is the process in HBase that involves combining smaller files into larger ones for efficiency. It helps in reducing the number of files and improving read and write performance in HBase.

Apache Storm integrates with the Hadoop ecosystem by running on Hadoop YARN. YARN (Yet Another Resource Negotiator) allows Storm to utilize Hadoop's resource management capabilities, making it easier to deploy and manage real-time processing applications alongside batch processing in a Hadoop cluster.

Sqoop is commonly used in Hadoop for importing data from relational databases into HDFS. It provides a command-line interface and supports the transfer of data between Hadoop and relational databases like MySQL, Oracle, and others.

UDFs (User Defined Functions) in Apache Pig play a crucial role in data transformation. They allow users to define their custom functions to process and transform data within Pig scripts, providing flexibility and extensibility in data processing operations.

Avro is suitable for schema evolution due to its support for schema evolution. It allows for the addition of new fields and the evolution of existing ones without requiring modifications to the existing data. This flexibility is crucial in evolving data structures in a Hadoop environment.

High availability for the NameNode is achieved in advanced setups through an Active-Passive configuration. In this setup, one NameNode is active, while the other remains passive, ready to take over in case of a failure. This ensures uninterrupted NameNode services and minimizes downtime.

The Resource Manager in Hadoop cluster capacity planning plays a crucial role in resource allocation. It is responsible for managing and allocating resources across the cluster, ensuring that computing resources are efficiently distributed among different applications and tasks. This is essential for optimal performance and utilization of the Hadoop cluster.

Data Replication is a critical factor in designing a disaster recovery plan for high availability in Hadoop. By replicating data across multiple nodes, Hadoop ensures that there are redundant copies of the data, reducing the risk of data loss in case of node failure. This redundancy enhances fault tolerance and supports disaster recovery efforts.