Developed in the AMPLab of the University of California, Berkeley, Apache Spark was developed for high speed, ease of use, and more in-depth analysis. Though it was built to be installed on top of the Hadoop cluster, its ability to perform parallel processing allows it to run independently as well.

Apache Spark has the following features:

  • Fast processing: The most important feature of Apache Spark that has made the big data world choose this technology over others is its speed. Big data is characterized by its volume, variety, velocity, value, and veracity due to which it needs to be processed at a higher speed. Spark contains Resilient Distributed Datasets (RDDs) that save the time taken in reading and writing operations, and hence it runs almost 10–100 times faster than Hadoop.
  • Flexibility: Apache Spark supports multiple languages and allows developers to write applications in Java, Scala, R, or Python. Equipped with over 80 high-level operators, this tool is quite rich from this aspect.
  • In-memory computing: Spark stores data in the RAM of servers, which allows it to access data quickly, and in-turn this accelerates the speed of analytics.
  • Real-time Computation: Spark is able to process real-time streaming data. Unlike MapReduce, which processes the stored data, Spark is able to process the real-time data and hence is able to produce instant outcomes.
  • Better analytics: Contrasting to MapReduce that includes Map and Reduce functions, Spark has much more in store. Apache Spark comprises a rich set of SQL queries, Machine Learning algorithms, complex analytics, etc. With all these Spark functionalities, Big Data Analytics can be performed in a better fashion.
  • Hadoop Integration: Spark is not only able to work independently; it can work on top of Hadoop as well. Not just this, it is certainly compatible with both versions of the Hadoop ecosystem.

Usage of Spark

  • Data integration: The data generated by systems are not consistent enough to combine for analysis. To fetch consistent data from systems we can use processes like Extract, transform, and load (ETL). Spark is used to reduce the cost and time required for this ETL process.
  • Stream processing: It is always difficult to handle the real-time generated data such as log files. Spark is capable enough to operate streams of data and refuses potentially fraudulent operations.
  • Machine learning: Machine learning approaches become more feasible and increasingly accurate due to enhancement in the volume of data. As spark is capable of storing data in memory and can run repeated queries quickly, it makes it easy to work on machine learning algorithms.
  • Interactive analytics: Spark is able to generate the respond rapidly. So, instead of running pre-defined queries, we can handle the data interactively.

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