Hadoop clusters use Block Replication to optimize data storage and access. Data is replicated across multiple nodes to ensure data availability and fault tolerance, allowing for efficient data retrieval and processing.

To optimize data processing, Hash partitioning in Hadoop can significantly improve the performance of MapReduce jobs. Hash partitioning ensures that related data is grouped together, reducing the amount of data shuffled between nodes during the MapReduce process and improving overall performance.

Sqoop achieves high throughput in data transfer using the Direct Mode, which allows direct communication between the Sqoop client and the database, bypassing the need for intermediate storage in Hadoop. This results in faster data transfers with reduced latency.

The feature of YARN that helps in improving the scalability of the Hadoop ecosystem is Horizontal Scalability. YARN allows for the addition of more nodes to the cluster, providing horizontal scalability and the ability to handle larger workloads efficiently.

The tool used for simulating cluster conditions on a single machine for testing is the MiniCluster. It allows developers to test their Hadoop applications in a controlled environment, simulating the behavior of a Hadoop cluster on a local machine for ease of debugging and testing.

MRUnit is a Java-based framework commonly used for unit testing in Hadoop applications. It allows developers to test their MapReduce programs in an isolated environment, making it easier to identify and fix bugs before deploying the code to a Hadoop cluster.

The implementation of a Combiner in a MapReduce job impacts performance by reducing the intermediate data volume. A Combiner combines the output of the Mapper phase locally on each node, reducing the data that needs to be transferred to the Reducer. This minimizes network traffic and improves overall job efficiency.

Apache Kafka handles high-throughput data streaming through the feature of topic partitioning. This allows Kafka to divide and parallelize the processing of data across multiple partitions, enabling scalability and efficient data streaming in Hadoop environments.

The choice of file format, such as Parquet or ORC, impacts performance through the storage format. These formats optimize storage and retrieval, affecting factors like compression, columnar storage, and efficient data serialization. The right format can significantly enhance query performance in analytics workloads.

Apache Oozie integrates with other Hadoop ecosystem components, such as Hive and Pig, through Action Nodes. These nodes define specific tasks, such as MapReduce, Pig, or Hive jobs, and orchestrate their execution as part of the workflow.