Optimizing MapReduce job performance involves considering the format of input data. Using input compression, such as Hadoop's default compression codecs, can reduce the amount of data transferred between nodes, improving job efficiency.

The Credential Store component in Hadoop's security architecture is responsible for storing and managing secret keys. It helps secure sensitive information such as credentials and encryption keys, enhancing the overall security of the Hadoop ecosystem.

Spark Structured Streaming in Apache Spark is used for processing large-scale streaming data in real-time. It provides a high-level API for stream processing with the same underlying engine as batch processing, offering ease of use and fault-tolerance.

Apache Tez is significant in optimizing Hadoop's data processing capabilities by introducing a more flexible and efficient data flow model. It enables the optimization of the execution plan, allowing tasks to be executed in a directed acyclic graph (DAG) fashion, improving overall performance and resource utilization.

The compaction mechanism in HBase helps in balancing the load across the cluster. It involves merging smaller HFiles into larger ones, optimizing storage and improving performance by reducing file fragmentation.

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.

Sqoop uses the Generic JDBC connector for efficient data import/export between Hadoop and specific Relational Database Management Systems (RDBMS). It provides a generic interface to interact with various databases, making it versatile and widely applicable.

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.

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 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.

For advanced Hadoop development, Apache Spark is crucial for integrating custom processing logic. Spark provides a powerful and flexible platform for big data processing, supporting advanced analytics, machine learning, and custom processing through its rich set of APIs.

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.