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.

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.

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.

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.

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.

When a MapReduce job is running slower than expected, the first debugging approach should prioritize examining the Mapper Code. Issues in the mapping phase can significantly impact job performance, and optimizing the mapper logic can lead to performance improvements.

Kafka is the preferred tool for ingesting streaming data from various sources in Hadoop when near-real-time data processing is required. It acts as a distributed, fault-tolerant, and scalable messaging system, efficiently handling real-time data streams.

Sqoop is a tool in the Hadoop ecosystem specifically designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases. It simplifies the process of importing and exporting data, bridging the gap between Hadoop and traditional databases.

When integrating Python with Hadoop, Hadoop Streaming is commonly used. It allows Python scripts to be used as mappers and reducers in a MapReduce job, enabling Python developers to leverage Hadoop's distributed processing capabilities.

The DAG Scheduler in Spark plays a crucial role in task execution by performing dependency analysis. It organizes tasks into stages based on their dependencies, optimizing the execution order and minimizing data shuffling. This is essential for efficient and parallel execution of tasks in Spark.

For in-depth analysis of Hadoop job performance, JProfiler is a tool that can be used to profile Java applications. It provides detailed insights into the behavior and performance of Java code, helping developers optimize their Hadoop jobs for better efficiency.

A 'Channel Selector' in Apache Flume is responsible for routing events to specific channels based on defined criteria. It enables the selective forwarding of events to different channels, allowing for customized handling and distribution of data within the Flume agent.