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.

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 'SUM' function in Apache Pig is used for aggregating data. It calculates the sum of values in a specified column, making it useful for tasks that involve summarizing and analyzing data.

Hive integrates with other components of the Hadoop ecosystem through User-Defined Functions (UDFs). These custom functions extend the functionality of Hive and enable users to perform complex analytics by incorporating their logic into the query execution process.