Hadoop Streaming API uses KeyValueTextInputFormat as an interface for data input and output. It allows handling key-value pairs, making it versatile for processing various data types in a streaming fashion.

Cascading's Lingual feature enables the execution of complex join operations in data processing pipelines. Lingual is a SQL interface for Cascading, making it easier to express complex data transformations.

Apache Oozie handles dependencies between multiple Hadoop jobs using a Directed Acyclic Graph (DAG). The DAG defines the order and dependencies between tasks, ensuring that the subsequent tasks are executed only when the prerequisite tasks are completed successfully.

YARN (Yet Another Resource Negotiator) enhances Hadoop's processing capabilities by introducing a separate ResourceManager for resource management. In earlier versions, the JobTracker handled both resource management and job scheduling, limiting scalability. With YARN, ResourceManager handles resource allocation, allowing more flexibility and scalability in processing tasks.

For loading real-time streaming data into Hadoop, Apache Kafka is the most appropriate ecosystem tool. Kafka is designed for high-throughput, fault-tolerant, and scalable data streaming, making it suitable for real-time data ingestion into Hadoop clusters.

In HDFS, data is read and written sequentially. Hadoop optimizes for large-scale data processing, and reading data sequentially enhances performance by minimizing seek time and maximizing throughput. This is particularly efficient for large-scale data analytics.

To achieve high throughput in data processing, emphasizing data partitioning is crucial. By efficiently partitioning data across nodes, Hadoop can parallelize processing, enabling high throughput and improved performance in scenarios with large datasets.

Off-heap memory configuration in a highly optimized Hadoop cluster helps improve garbage collection efficiency. By allocating memory outside the Java heap, it reduces the impact of garbage collection pauses on overall performance.

Ensuring efficient data processing in Hadoop involves focusing on input splitting during development. Input splitting is the process of dividing input data into manageable chunks, allowing parallel processing across nodes and optimizing job performance.

When a MapReduce job fails in Hadoop, the first step is typically to examine the logs. Hadoop generates detailed logs that provide information about the failure, helping developers identify the root cause and take corrective actions.