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.

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.

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.

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.

In a scenario with diverse data analytics applications, compute capacity is most critical in capacity planning. The cluster needs sufficient processing power to handle various computation-intensive tasks across different applications. Scalability is also essential to accommodate future growth.

The integration of Avro and Parquet improves data pipeline efficiency by combining Avro's schema evolution flexibility with Parquet's columnar storage and compression. Parquet's efficient compression reduces storage space, and Avro's support for schema evolution ensures consistency in data processing across the pipeline. This integration enhances both storage and processing efficiency in large-scale Hadoop environments.

The process of consolidating the output from Mappers in MapReduce is done by the Reducer component. Reducers receive the intermediate key-value pairs emitted by Mappers, perform aggregation, and produce the final output of the MapReduce job.

The integration of Apache Spark with MapReduce in Hadoop enhances the capability for handling big data analytics. It leverages Hadoop's distributed storage and processing capabilities, allowing users to combine the strengths of both technologies for efficient data processing.

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.

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.