Sqoop's Incremental Load feature enables the incremental import of data from a database. This means that only the new or updated records since the last import will be transferred, reducing the amount of data processed and improving efficiency.

Apache Oozie uses the Workflow Engine to interact with the Hadoop job tracker and execute jobs. The Workflow Engine coordinates the execution of actions and manages the workflow lifecycle, interacting with the underlying Hadoop ecosystem.

The administrator should focus on data replication. By ensuring that data is replicated across nodes, the impact of node failures can be mitigated. This approach enhances fault tolerance, as the loss of data on a single node can be compensated by its replicated copies on other nodes in the cluster.

Apache Ambari is commonly used for deploying and managing Hadoop clusters. It provides a web-based interface for cluster provisioning, monitoring, and management, making it easier for administrators to set up and maintain Hadoop environments.

Avro is preferred in scenarios with frequent schema modifications due to its support for schema evolution. Avro allows for the flexible addition and removal of fields, making it easier to handle changes in the data structure without breaking compatibility. This feature is crucial in dynamic environments where the schema evolves over time.

Using compression in Hadoop development can significantly reduce the amount of data transferred between Map and Reduce phases. Compression techniques help minimize the data size, leading to faster data transfer and more efficient processing in Hadoop.

Spark Streaming is the component of Apache Spark that enables the efficient processing of streaming data. It provides a high-level API for stream processing, allowing real-time analysis of data streams in the Spark framework.

In MapReduce, the Input phase is responsible for preparing the data for processing by the Mapper. During this phase, input data is read and split into key-value pairs, which are then processed by the Mapper function.

Apache Impala differs from Hive in terms of data processing by utilizing in-memory processing. Impala is designed for low-latency SQL queries on Hadoop data, and it processes data in-memory, providing faster query performance compared to traditional Hive queries.

A Replication strategy is essential to handle node failures in a Hadoop cluster. HDFS uses replication to ensure fault tolerance by storing multiple copies (replicas) of data across different nodes in the cluster. This redundancy helps in recovering from node failures.