The Custom KeyValue InputFormat in Hadoop is used when standard InputFormats do not meet specific data processing needs. It allows for custom parsing of key-value pairs, providing flexibility in handling various data formats.

MapReduce is a distributed computing paradigm used in Big Data applications for processing large datasets. It involves the Map and Reduce phases, enabling parallel and distributed processing of data across a Hadoop cluster.

In the Mapper class, the method called for each key/value pair in the input data is map(). The map() method is responsible for processing the input and emitting intermediate key-value pairs, which are then sorted and passed to the Reducer.

Apache Ambari contributes to the Hadoop ecosystem by providing cluster management and monitoring capabilities. It simplifies the installation, configuration, and management of Hadoop clusters, making it easier for administrators to handle complex tasks related to cluster operations.

A common first step in troubleshooting an unresponsive DataNode is to check network connectivity. Network issues can lead to communication problems between nodes, impacting the DataNode's responsiveness. Ensuring proper connectivity is crucial for the smooth operation of a Hadoop cluster.

Advanced security configurations in Hadoop often involve using Apache Ranger for fine-grained access control. Apache Ranger provides centralized security administration and fine-grained access policies, enabling administrators to define and manage access controls for Hadoop components.

YARN's ResourceManager handles large-scale applications differently from Hadoop 1.x's JobTracker by employing dynamic resource allocation. It dynamically allocates resources to applications based on their needs, optimizing resource utilization and improving scalability compared to the fixed assignment in Hadoop 1.x.

In complex data analysis, the Data Flow in Apache Pig helps in managing multiple data sources and sinks. It defines the sequence of operations applied to the data, facilitating efficient processing and transformation of data across various stages of the analysis pipeline.

Secure data transmission in Hadoop is often achieved through the use of encryption. This process involves encoding data to make it unreadable without the appropriate decryption key, ensuring that data is transmitted and stored securely.

Integrating Apache Flume with Kafka Source enables effective large-scale data analysis. Kafka acts as a distributed messaging system, allowing seamless data transfer between Flume and other tools in the Hadoop ecosystem, facilitating scalable data processing.