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 concept of Data Localization is crucial in designing a Hadoop cluster. It involves placing data close to where it is most frequently accessed, reducing latency and improving overall system performance. Efficient data processing and resource utilization are achieved by strategically placing data across the cluster.

For advanced Hadoop clusters, Apache Spark is a technique used to enhance processing capabilities for complex data analytics. Spark provides in-memory processing, iterative machine learning, and interactive queries, making it suitable for advanced analytics tasks.

When a file in HDFS is smaller than the Hadoop block size, padding is applied. The file does not occupy the entire block, and the remaining space is padded with zeros. This approach ensures uniformity in block size, simplifying data management and storage.

Parquet is the preferred file format for large-scale data processing in Hadoop due to its columnar storage, compression techniques, and schema evolution support. It offers high performance for analytical queries and is well-suited for data warehouse applications.

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.

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.