The Snappy compression codec in Hadoop is renowned for its high compression ratio and fast decompression speed. It is particularly suitable for scenarios where low latency is crucial, making it a popular choice for big data processing.

Large tables in HBase are divided and distributed across the cluster based on Row-Key Partitioning. Rows with similar Row-Key values are grouped together, and the distribution is determined by the Row-Key, facilitating efficient data retrieval and parallel processing.

The approach for building a Hadoop data pipeline for real-time analytics from social media data streams involves using Apache Sqoop for ingestion, Apache Storm for processing real-time data, and Apache HDFS for storage. This combination ensures efficient data transfer, real-time processing, and scalable storage.

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 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.