Diff between Hadoop and Spark

Hadoop and Spark are both widely used technologies in the field of big data processing, but they have different purposes and characteristics:

1. Purpose:

• • Hadoop: Hadoop is an open-source framework designed for the distributed storage and processing of large datasets. It consists of two main components: Hadoop Distributed File System (HDFS) for storage and MapReduce for batch processing.
  • Spark: Spark is also an open-source framework that focuses on in-memory data processing and provides a more flexible and efficient way to process data compared to Hadoop’s MapReduce. Spark supports various data processing tasks, including batch processing, interactive queries, machine learning, and graph processing.

1. Processing Model:

• • Hadoop: Hadoop’s primary processing model is MapReduce, which processes data in two phases: the map phase for data transformation and filtering and the reduce phase for aggregation and summarization. Hadoop is suitable for batch processing.
  • Spark: Spark provides a more versatile processing model with its Resilient Distributed Dataset (RDD) abstraction. RDDs allow for in-memory data processing, making Spark significantly faster than Hadoop for iterative algorithms and interactive data analysis.

1. Performance:

• • Hadoop: Hadoop’s MapReduce paradigm can be slower for iterative and interactive tasks due to its reliance on disk I/O between processing stages.
  • Spark: Spark’s in-memory processing minimizes the need for data to be read from and written to disk between stages, resulting in faster execution times for many workloads.

1. Ease of Use:

• • Hadoop: Hadoop requires more complex configuration and setup, mainly when dealing with cluster management and job optimization.
  • Spark: Spark’s APIs (in languages like Scala, Java, Python, and R) are generally considered more user-friendly, and they offer higher-level abstractions like DataFrames and Datasets, which make coding tasks easier.

1. Ecosystem and Libraries:

• • Hadoop: Hadoop has a mature ecosystem with various projects like Hive, Pig, and HBase, which enable different types of data processing and storage.
  • Spark: While Spark’s ecosystem is less extensive than Hadoop’s, it is rapidly growing. Spark supports various libraries for machine learning (MLlib), graph processing (GraphX), and SQL queries (Spark SQL), among others.

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