Apache Kafka complements Hadoop by enabling stream processing. Kafka serves as a distributed, fault-tolerant messaging system that allows seamless ingestion and processing of real-time data, making it an ideal component for building robust and scalable data pipelines alongside Hadoop.

In a data warehousing project with complex transformations, Hive with custom UDFs would be more suitable. Hive, with its extensibility through custom User-Defined Functions (UDFs), allows for the implementation of complex transformations on the data, making it a better choice for scenarios requiring custom processing logic.

Apache Hadoop YARN (Yet Another Resource Negotiator) is the framework responsible for managing resources and job scheduling in Hadoop clusters. It provides an efficient and scalable framework for testing Hadoop application performance under varying data loads by dynamically allocating resources based on workload requirements.

The NameNode in Hadoop is responsible for metadata management, storing information about the location and health of data blocks. It doesn't store the actual data but keeps track of where data is stored across the cluster. This metadata is crucial for the proper functioning of the Hadoop Distributed File System (HDFS).

In Flume, the Channel Selector mechanism allows for dynamic data routing and transformation. It helps in directing incoming data to different channels based on specified criteria, enabling flexibility in data processing and handling.

Data Serialization in Hadoop is crucial for optimizing read/write operations on large datasets. Serialization is the process of converting complex data structures into a format that can be easily transmitted or stored. In Hadoop, this optimization helps in efficient data transfer and storage.

In HiveQL, the EXPLAIN command is used to show the query execution plan. It provides insights into how Hive intends to execute the given query, including the sequence of tasks and operations involved. Analyzing the execution plan helps optimize queries for better performance.

In HBase, the RowKey column is used to uniquely identify each row in a table. It serves as the primary key and is crucial for efficient data retrieval in HBase tables.

Advanced Big Data analytics often employ Machine Learning techniques for predictive modeling and analysis. Machine Learning algorithms enable systems to learn and make predictions or decisions based on data patterns, contributing to advanced analytics in Big Data applications.

The RegionServer in HBase is responsible for storage and retrieval operations. It manages the actual data blocks, handling read and write requests, and communicates with the HBase Master to perform various tasks such as load balancing and failover.

The initial step in setting up a Hadoop cluster is to install the Hadoop software on all nodes. This involves downloading the Hadoop distribution, configuring environmental variables, and ensuring that the software is present on each machine in the cluster.

The default input format for a MapReduce job in Hadoop is TextInputFormat. It treats input files as plain text files and provides key-value pairs, where the key is the byte offset of the line, and the value is the content of the line.