When a MapReduce job fails in Hadoop, the first step is typically to examine the logs. Hadoop generates detailed logs that provide information about the failure, helping developers identify the root cause and take corrective actions.

Snappy compression codec in Hadoop is known for providing a good balance between compression ratio and speed. It offers relatively fast compression and decompression while achieving a reasonable compression ratio, making it suitable for various use cases.

In HDFS, data is read and written sequentially. Hadoop optimizes for large-scale data processing, and reading data sequentially enhances performance by minimizing seek time and maximizing throughput. This is particularly efficient for large-scale data analytics.

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 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.

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.

In scenarios with iterative machine learning algorithms, Spark MLlib would be most beneficial. MLlib is Spark's machine learning library that provides high-level APIs for machine learning tasks, including iterative algorithms commonly used in machine learning workflows.

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.

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.

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.