A Hadoop administrator should prioritize data in different HDFS directories based on the replication factor. Critical data should have a higher replication factor to ensure availability and fault tolerance in the event of node failures.

Parquet is a highly efficient file format in Hadoop designed for fast data serialization and deserialization. It is columnar-oriented, supports schema evolution, and is optimized for both compression and performance.

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.

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.

Sink Groups is a key feature in Flume that allows for load balancing and failover among multiple sinks. It enables the distribution of events across different sinks, ensuring efficient load distribution and providing fault tolerance through failover mechanisms.