What is the difference between Hadoop and Spark?

Hadoop and Apache Spark are both distributed computing frameworks, but they serve different purposes and have distinct characteristics. Here are the key differences between Hadoop and Spark:

1. Processing Model:

   • Hadoop: Primarily designed for batch processing. It uses MapReduce as its processing model, where data is processed in two phases - map and reduce.
   • Spark: Supports both batch processing and real-time stream processing. It provides a more flexible processing model with the ability to build complex workflows.

2. Performance:

   • Hadoop: MapReduce can be relatively slow for iterative algorithms and interactive data analysis due to the disk-based nature of intermediate data storage.
   • Spark: Spark processes data in-memory, leading to significantly faster performance compared to Hadoop's MapReduce, especially for iterative algorithms and interactive data analysis.

3. Ease of Use:

   • Hadoop: Requires developers to write complex and verbose MapReduce programs in Java, which can be challenging and time-consuming.
   • Spark: Offers high-level APIs in Java, Scala, Python, and R, making it more user-friendly and accessible. It also has a built-in interactive shell for ad-hoc querying.

4. Data Processing:

   • Hadoop: Stores data in Hadoop Distributed File System (HDFS) and processes it using MapReduce jobs.
   • Spark: Can process data from various sources, including HDFS, HBase, Amazon S3, and more. It is not tied to a specific storage system.

5. Data Caching:

   • Hadoop: Relies on the disk for intermediate data storage between Map and Reduce phases.
   • Spark: Utilizes in-memory caching, allowing iterative algorithms to be more efficient by keeping intermediate data in memory.

6. Built-in Libraries:

   • Hadoop: Provides a limited set of built-in libraries for common data processing tasks.
   • Spark: Offers a rich set of libraries, including Spark SQL for structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for real-time data processing.

7. Fault Tolerance:

   • Hadoop: Achieves fault tolerance through data replication in HDFS.
   • Spark: Also achieves fault tolerance but uses a different mechanism called lineage information and resilient distributed datasets (RDDs).

In summary, while Hadoop and Spark share similarities as distributed computing frameworks, Spark is generally considered more versatile, faster, and user-friendly, making it suitable for a broader range of data processing tasks, including both batch and real-time processing.