A key difference between Avro and Parquet is how they handle data processing. Avro focuses on schema evolution, while Parquet excels in partitioning data. Parquet allows for efficient pruning and retrieval of specific data partitions, enhancing query performance.

The Oozie SLA (Service Level Agreement) feature plays a crucial role in ensuring timely execution of workflows. It allows users to define performance expectations, and Oozie monitors and enforces these expectations, triggering alerts or actions if SLAs are not met.

Avro is a common data serialization format in Hadoop used for efficient data transformation. It provides a compact binary format and is schema-aware, making it suitable for diverse data types and enabling efficient data processing in Hadoop ecosystems.

The advantage of using PySpark is simplified development. Python is known for its simplicity and readability, making it easier for developers to write and maintain code, resulting in increased productivity in comparison to the complexities of conventional MapReduce jobs.

When addressing 'Garbage Collection' issues in Java-based Hadoop applications, adjusting the Heap size parameter is a key strategy. Garbage Collection is the process of automatically reclaiming memory occupied by objects that are no longer in use, and adjusting the Heap size helps optimize memory management in Hadoop applications.

In Hadoop, the NameNode is responsible for storing metadata about files and directories in HDFS. It keeps track of the location and health of data blocks, playing a crucial role in the overall architecture of Hadoop's distributed file system.

In a scenario where a Hadoop cluster is exposed to a public network, implementing firewalls is crucial to control and monitor incoming and outgoing traffic. Firewalls act as a barrier between the public network and the Hadoop cluster, enhancing security by allowing only authorized communication.

Avro would be more advantageous for time-series data analysis in a Hadoop-based project. Avro's compact binary format and schema evolution support make it well-suited for efficient serialization and deserialization of time-series data, essential for analytics in large datasets.

Implementing Data Compression in Hadoop is a best practice for optimizing data storage and retrieval. Compression reduces the amount of storage space required for data and improves the efficiency of data transfer across the network, resulting in overall performance enhancement.

The primary role of Apache Hive in the Hadoop ecosystem is data querying. Hive provides a SQL-like language called HiveQL that allows users to query and analyze data stored in Hadoop. It translates HiveQL queries into MapReduce jobs, making it easier for users familiar with SQL to work with big data.

Checkpointing is essential for maintaining data consistency and reliability in distributed Hadoop data pipelines. It involves creating periodic checkpoints to save the current state of the application, enabling recovery from failures without reprocessing the entire dataset.

MRUnit is most commonly used for Unit Testing in the Hadoop ecosystem. It provides a framework for writing and running unit tests for MapReduce jobs, allowing developers to validate the correctness of their code in a controlled environment.