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 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.

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.

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.

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.

In the context of optimizing Hadoop applications, data compression plays a significant role in reducing network traffic. Compressing data before transferring it between nodes reduces the amount of data that needs to be transmitted, resulting in faster and more efficient data processing in the Hadoop cluster.

ResourceManager is used for scheduling and managing user jobs in a Hadoop cluster. It works in conjunction with the NodeManagers to allocate resources and monitor the execution of tasks on the cluster.

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.

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.

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.