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.

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 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.

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.

When dealing with sensitive data, data encryption becomes a crucial aspect of security in Hadoop. Encrypting data at rest and in transit ensures that unauthorized access is prevented, providing an additional layer of protection for sensitive information.

In MapReduce, the process of breaking down large datasets into smaller, manageable chunks for individual mappers is called Input Splitting. These splits are then processed in parallel by the Mapper tasks to achieve distributed computing and efficient data processing.

Batch processing jobs in Hadoop are typically scheduled using Apache Oozie. Oozie is a workflow scheduler that manages and schedules Hadoop jobs, providing a way to coordinate and automate the execution of complex workflows.

YARN's Multi-Tenant mode enables the running of multiple workloads simultaneously on a shared cluster. It allows different applications to share cluster resources efficiently, supporting a diverse set of workloads.

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.

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.

For real-time analysis of streaming data, Spark Streaming is more suitable than traditional MapReduce. Spark Streaming allows processing and analyzing data in real-time batches, making it ideal for scenarios like social media streaming data analysis where quick insights are crucial.

Hadoop's Rack Awareness feature optimizes cluster efficiency and data locality by strategically placing replicated data blocks on different racks. This reduces the risk of data loss due to rack failure and enhances overall fault tolerance, ensuring that data is available even in the face of hardware failures.