Apache Flume is highly suitable for event-driven data ingestion into Hadoop due to its fault tolerance. It can reliably collect and transport large volumes of data, ensuring that data is not lost even in the presence of node failures or network issues.

For high-speed data querying and analysis, Apache Impala is crucial. Impala provides low-latency SQL queries directly on Hadoop data, allowing for real-time analytics without the need for data movement. It is suitable for scenarios where rapid and interactive analysis of large datasets is required.

The Hadoop Streaming API handles different data formats through serialization. Serialization is the process of converting complex data structures into a format that can be easily stored, transmitted, or reconstructed. It allows Hadoop to work with various data types and ensures compatibility during the MapReduce process.

Batch processing typically has higher latency than real-time processing. In batch processing, data is collected and processed in predefined intervals, leading to delays, while real-time processing handles data as it arrives, reducing latency.

A Configuration in Apache Flume specifies the movement of data from a source to a sink. It defines the settings and parameters for the Flume agents, allowing users to customize the behavior of the data flow within the Flume pipeline.

The Hadoop Federation feature contributes to disaster recovery and data management by improving fault tolerance. Hadoop Federation allows the distribution of namespace across multiple NameNodes, reducing the risk of a single point of failure. In the event of a NameNode failure, other NameNodes can continue to operate, contributing to a more robust disaster recovery strategy.

Resource Containers are key to YARN's ability to support multiple processing models on a single system. They encapsulate the allocated resources and are used to execute tasks across the cluster in a flexible and efficient manner.

Apache Hive uses a Metastore to store the schema information for tables. The Metastore is a centralized repository that stores metadata, including table schemas, partition information, and storage location. This separation of metadata from data allows for better organization and management of data in Hive.

The use of a Schema Registry in Avro is crucial for ensuring data compatibility across different versions. It acts as a central repository for storing and managing schemas, allowing different components in a Hadoop ecosystem to access and interpret data consistently.

The ResourceManager is a key component in a Hadoop cluster for managing and monitoring system health and fault tolerance. It manages the allocation of resources and schedules tasks across the cluster, ensuring efficient resource utilization and fault tolerance.