Authentication is the process by which Hadoop ensures that a user or service is actually who they claim to be. It involves verifying the identity of users or services before granting access to the Hadoop cluster.

HDFS ensures data integrity during transmission through checksum verification. Each block of data is associated with a checksum, and the checksums are verified during read operations to detect and correct any data corruption that may have occurred during transmission. This mechanism enhances the reliability of data stored in HDFS.

Sqoop is the tool in the Hadoop ecosystem best known for data ingestion from various sources into HDFS. It simplifies the transfer of data between Hadoop and relational databases, facilitating the import and export of data in a Hadoop cluster.

For large-scale Hadoop deployments, Snapshot strategies are essential for efficient and rapid disaster recovery. Snapshots capture the current state of the Hadoop file system, allowing administrators to create a point-in-time copy of the data. This enables quick recovery in case of data corruption or loss, ensuring minimal downtime.

The Secondary NameNode in HDFS is not a backup node but is responsible for performing periodic checkpoints of the file system metadata. It creates a merged, updated checkpoint from the edits log and the current metadata, reducing the time needed for the NameNode to recover in case of failure.

Sqoop's Export tool is used to export data from HDFS back to a relational database. This tool is essential for moving data from Hadoop to a relational database for further analysis or reporting.

Apache Flume is highly suitable for event-driven data ingestion into Hadoop due to its fault tolerance. It can reliably collect and transport large volumes of data, ensuring that data is not lost even in the presence of node failures or network issues.

For high-speed data querying and analysis, Apache Impala is crucial. Impala provides low-latency SQL queries directly on Hadoop data, allowing for real-time analytics without the need for data movement. It is suitable for scenarios where rapid and interactive analysis of large datasets is required.

The Hadoop Streaming API handles different data formats through serialization. Serialization is the process of converting complex data structures into a format that can be easily stored, transmitted, or reconstructed. It allows Hadoop to work with various data types and ensures compatibility during the MapReduce process.

Batch processing typically has higher latency than real-time processing. In batch processing, data is collected and processed in predefined intervals, leading to delays, while real-time processing handles data as it arrives, reducing latency.