Apache Hadoop and Apache Spark are both popular frameworks used for big data processing, but they have some key differences in terms of their architecture and use cases:

Apache Hadoop:

1. MapReduce Paradigm: Hadoop is primarily associated with the MapReduce programming model, which we discussed earlier. It is designed for batch processing of large datasets. Hadoop Distributed File System (HDFS) is the storage component used to store data in a distributed manner.

2. Batch Processing: Hadoop is well-suited for batch processing tasks where data is processed in chunks or batches. It is suitable for tasks like log analysis, ETL (Extract, Transform, Load) processes, and batch analytics.

3. Java-Centric: Hadoop is often associated with Java, and most of its core components are implemented in Java. However, there are projects like Apache Pig and Apache Hive that provide higher-level abstractions and allow you to write Hadoop jobs in languages other than Java.

Apache Spark:

1. In-Memory Processing: Apache Spark, on the other hand, is designed for both batch and real-time data processing. It leverages in-memory computing to perform iterative algorithms and interactive queries much faster than Hadoop MapReduce.

2. General-Purpose: Spark is more versatile than Hadoop and supports a wide range of data processing tasks, including batch processing, interactive queries, machine learning, and stream processing. It provides high-level APIs in multiple programming languages, making it accessible to a broader audience.

3. Resilient Distributed Datasets (RDDs): Spark introduces the concept of RDDs, which are in-memory distributed data structures. RDDs allow for iterative and interactive data processing, which is more challenging to achieve with Hadoop’s MapReduce model.

4. Unified Platform: Spark includes libraries for various data processing tasks, such as Spark SQL for structured data, Spark MLlib for machine learning, and Spark Streaming for real-time data processing. This makes it a unified platform for big data processing.