What is the difference between Spark and Hadoop?

Spark and Hadoop are two different technology frameworks for big data processing. Here are some differences between them:

1. Data processing models: Hadoop utilizes a batch processing model, while Spark utilizes a real-time processing model. Hadoop divides data into small blocks and processes them in batches using the MapReduce algorithm. In contrast, Spark uses Resilient Distributed Datasets (RDD) and Directed Acyclic Graph (DAG) model, allowing for real-time data processing.
2. Memory usage: Hadoop stores data on disk and writes and reads data to and from disk at the beginning and end of each computing task. Spark, on the other hand, maintains data in memory and utilizes in-memory computation to accelerate processing speed, making it faster than Hadoop.
3. Processing Speed: Spark is faster than Hadoop because it can store data in memory and perform calculations using the DAG model. Spark also offers advanced features such as built-in machine learning libraries and graph computation libraries, which can further speed up data processing.
4. Execution Engine: Hadoop utilizes MapReduce as its primary execution engine, while Spark uses Spark Core as its engine. Spark also offers other execution engines, such as Spark SQL, Spark Streaming, and MLlib, to support various types of data processing tasks.
5. Ecosystem: Hadoop boasts a mature ecosystem comprising HDFS (Hadoop Distributed File System), YARN (Resource Manager), and various tools and libraries. Similarly, Spark has its own ecosystem, which includes Spark SQL, Spark Streaming, GraphX, MLlib, and other libraries.

In summary, Hadoop is suitable for scenarios that involve processing large amounts of data in batches, while Spark is suitable for scenarios that require faster, real-time processing of big data and offer more advanced features and execution engine options.