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.

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.

Rack Awareness in a Hadoop cluster helps balance the load and improve data locality. It ensures that data blocks are distributed across nodes in a way that considers the physical location of nodes in different racks, reducing network traffic and enhancing performance.

In a scenario involving the integration of streaming and batch data processing, Apache Kafka is most effective. Kafka provides a distributed messaging system, allowing seamless communication between streaming and batch processing components in the Hadoop ecosystem, ensuring reliable and scalable data integration.

For real-time data processing with Hadoop in Java, Apache Storm is typically employed. Storm is a distributed real-time computation system that seamlessly integrates with Hadoop, allowing for the processing of streaming data in real-time.

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.

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.

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.

Apache Flume is essential for data ingestion in a basic Hadoop data pipeline. It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data from various sources to Hadoop's distributed file system.

Coordinators in Apache Oozie have the significance of task scheduling. They enable the definition and scheduling of recurrent workflows based on time and data availability. Coordinators ensure that workflows are executed at specified intervals or when certain data conditions are met.

In a scenario involving streaming data, Apache Flink is a suitable Hadoop ecosystem component. Apache Flink is designed for stream processing, offering low-latency and high-throughput data processing capabilities, making it well-suited for real-time analytics on streaming data.

Inconsistent data processing speeds across the cluster may be due to various factors. To diagnose and resolve the issue, the Hadoop administrator should monitor resource utilization, including CPU, memory, and disk usage, to identify bottlenecks and optimize cluster performance.