In the Hadoop Streaming API, custom scripts are often used to optimize the mapping and reducing processes. These scripts, usually written in languages like Python or Perl, allow users to define their own logic for data transformation, filtering, and aggregation, providing flexibility and customization in Hadoop data processing.

Reflection is a key feature in Avro that facilitates data serialization and deserialization in a distributed environment. It enables automatic generation of code for serialization and deserialization, simplifying the process of working with complex data structures.

HBase's in-memory storage feature makes it suitable for real-time read/write access. The data is stored in memory, enabling faster access for read and write operations, making it well-suited for applications requiring low-latency responses.

The JobTracker is an essential component in Hadoop for tracking job processing and resource utilization. It manages and schedules MapReduce jobs, tracks the progress of tasks, and monitors resource usage in the cluster. It plays a crucial role in coordinating job execution across the nodes.

The number of DataNodes in a Hadoop cluster is crucially influenced by storage capacity. It determines how much data can be stored and processed concurrently across the cluster. Ensuring sufficient storage capacity is essential for optimal performance and data processing capabilities.

The Failover Controller in a Hadoop cluster is responsible for detecting and handling the failure of DataNode machines. It ensures that data availability is maintained by redirecting tasks to healthy DataNodes when a failure occurs.

Spark is a popular Scala-based tool for interactive data analytics with Hadoop. It provides a fast and general-purpose cluster computing framework for big data processing, making it suitable for various data processing tasks.

The default block size used by Hadoop Distributed File System (HDFS) for storing data is 128 MB. This block size is configurable but is set to 128 MB in many Hadoop distributions as it provides a balance between storage efficiency and parallel processing.

The 'SORT BY' clause in Apache Pig is used for sorting data in a dataset based on one or more fields. It arranges the data in ascending or descending order, providing flexibility in handling sorted data for further processing.

In Spark, Resilient Distributed Datasets (RDDs) are immutable collections of data items distributed over a cluster. RDDs are the fundamental data structure in Spark, providing fault tolerance and parallel processing capabilities.

SecurityManager is a critical component in Hadoop's architecture, responsible for ensuring secure authentication and authorization within the Hadoop cluster. It plays a crucial role in protecting the integrity and confidentiality of the data.

Speculative Execution is a technique employed for time-sensitive data processing in MapReduce. It involves running duplicate tasks on different nodes and using the result from the first one to finish. This helps mitigate delays caused by slow-performing tasks.