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.

The most effective Oozie feature in this scenario is the Coordinator. Coordinators in Oozie allow you to define and manage time-based schedules for recurring jobs. They are well-suited for situations where data processing needs to be scheduled after data loading is completed, ensuring timely execution based on specified intervals.

The role of a combiner in the MapReduce framework is partial aggregation. It performs a local reduction of data on each mapper node before sending it to the reducer. This reduces the volume of data transferred over the network and improves the efficiency of the data transformation process.

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.

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.

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.