Spark vs Hadoop

In this document we will try to understand why spark is better than hadoop

Spark is often considered to be an improvement over Hadoop MapReduce, the original big data processing framework, for several reasons:

1. Speed: Spark is much faster than Hadoop MapReduce for both batch and real-time processing. This is because Spark uses in-memory computing for data processing, while Hadoop MapReduce uses disk-based storage. This means that Spark can process data much faster because it doesn't need to read and write data from disk.

2. Ease of use: Spark has a simpler programming model than Hadoop MapReduce. It provides high-level APIs in Java, Scala, and Python, which make it easier to develop big data applications. Hadoop MapReduce, on the other hand, requires developers to write complex Java code to implement the map and reduce functions.

3. Flexibility: Spark supports a wide range of data processing tasks, including batch processing, interactive queries, streaming, machine learning, and graph processing. Hadoop MapReduce, on the other hand, is primarily designed for batch processing.

4. Complex data processing: Spark's SQL and DataFrame API's allow for more complex data processing tasks which are not possible in Hadoop MapReduce

5. Better fault tolerance: Spark uses a technology called Resilient Distributed Datasets (RDD) which allows for fault tolerance. RDDs can recover lost data by recomputing the missing data on the fly, so that the processing can continue even if some of the data is lost. Hadoop MapReduce, on the other hand, requires the entire job to be restarted from the beginning if there is a failure.

6. Improved Cluster Management: Spark includes an in-built cluster manager, which makes it easier to manage the Spark cluster. Hadoop, on the other hand, requires the use of a separate cluster manager like Apache Mesos or Hadoop YARN.

However, it's worth noting that Hadoop and Spark are not mutually exclusive and can complement each other. For example, Hadoop's distributed file system (HDFS) can be used as a storage layer for Spark.