Partitioning data in HDFS improves processing efficiency by enhancing data locality. This means that computation is performed on nodes where the data is already stored, reducing the need for extensive data movement across the network and thereby improving overall processing speed.

Snapshot recovery techniques in Hadoop allow for the restoration of data to a specific point in time. Snapshots capture the state of the HDFS at a particular moment, providing a reliable way to recover data to a known and consistent state.

Apache Hive is primarily used for building data pipelines involving SQL-like queries in the Hadoop ecosystem. It provides a high-level query language, HiveQL, that allows users to express queries in a SQL-like syntax, making it easier for SQL users to work with Hadoop data.

Apache Storm distinguishes itself in the Hadoop ecosystem by specializing in stream processing. It is designed to handle real-time data streaming and enables the processing of data as it arrives, making it suitable for applications that require low-latency and continuous data processing.

Ambari plays a critical role in managing and monitoring Hadoop clusters. It provides an intuitive web-based interface for administrators to configure, manage, and monitor Hadoop services, ensuring the health and performance of the entire cluster.

In Hadoop, the traditional framework used for batch processing is Apache Hadoop MapReduce. It is a programming model and processing engine that enables the processing of large datasets in parallel across a distributed cluster.

Apache Storm is a tool in Hadoop designed for efficient real-time stream processing. It allows for the processing of data in motion, making it suitable for scenarios where low-latency and real-time insights are crucial.

The primary role of Apache Sqoop in the Hadoop ecosystem is data ingestion. Sqoop facilitates the transfer of data between Hadoop and relational databases, making it easier to import and export structured data. It helps bridge the gap between the Hadoop Distributed File System (HDFS) and relational databases.

The primary benefit of using Avro in Hadoop ecosystems is high compression. Avro employs a compact binary format that results in efficient storage, reducing the amount of disk space required for storing data. This is especially crucial for handling large datasets in Hadoop environments.

Hadoop's HDFS High Availability feature employs a Standby NameNode to handle the failure of the primary NameNode. The Standby NameNode maintains a synchronized copy of the metadata, ready to take over in case the primary NameNode fails, ensuring continuous availability.