What proves that Apache Spark is better than Hadoop?

What proves that Apache Spark is better than Hadoop?

There are certain cases where Apache Spark surpasses Hadoop. In this article, our experts will share their reviews about the things that make Apache Spark a superior choice over Hadoop.

Apache Spark is lightning fast cluster computing tool used by developers and programmers. This tool is up to 100 times faster than Hadoop MapReduce since it features faster-in-memory data analytics processing power. It is a Big Data framework that is used as a general purpose data processing engine on top of HDFS. Professionals find Apache Spark ideal for meeting several data processing requirements ranging from Batch processing to data streaming.

Hadoop is an open source framework used for processing data stored inside HDFS. Using Hadoop, programmers can process structured, unstructured, or semi-structured data with an ease. However, the data process can be done only in Batch mode.

Both Apache Spark and Hadoop share differences in terms of –

  • Speed
  • Difficulty
  • Convenience
  • Real-time analysis
  • Latency
  • Interactive mode
  • Streaming
  • Ease of use
  • Scheduler
  • Fault tolerance
  • Recovery
  • License
  • OS support
  • Programming language support
  • Scalability
  • The line of code
  • Caching
  • Machine learning
  • Hardware requirements
  • Community

#Speed- Apache Spark runs apps up to 100 times faster in memory and 10 times faster on disk than hadoop.

#Difficulty – Spark is easy to program since it contains lots of high-level operators with RDD

#Convenience- Programmers can easily perform batching, interactive and machine learning and streaming in one cluster. This makes it a complete data analytics engine. When programmers install Spark on a cluster, it will be enough to manage all the requirements with ease.

#Real-time analysis – Spark can process real time data.

#Latency – Spark offers low-latency computing

#Interactive mode – Programmers can use Spark for processing data interactively

#Streaming – Spark can help process real time data with its Streaming feature.

#Ease-of-use – Spark is easier to use, thanks to RDD and its APIs.

#Scheduler – As spark has in-memory computation, it acts its own flow scheduler

#Fault tolerance- Spark is fault-tolerant, i.e. there is no requirement to restart the app from scratch if it fails.

#Recovery – RDDs enables recovery of partitions on failed nodes with the help of DAG.

#Language developed – Spark is built on Scala

#OS support – Spark supports cross-platform

#Programming language support – Sparks supports Scala, Java, R, SQL, and Python

#Scalability – Apache Spark is extremely scalable.

#the line of code – Merely 20000 line of codes are used while developing Apache Spark

#Caching – Spark can cache data in memory and enhance the system performance

#Machine learning – Spark consists its own machine learning set – MLlib

#Hardware requirements – Spark requires mid to high level hardware

#Community – Spark is the most active project at Apache and has stronger community.

Ecosystem of Apache Spark

There are total six components that complete the ecosystem of Apache Spark. These are as under-

  • Spark core
  • Spark SQL
  • Spark Streaming
  • Spark MLlib
  • Spark GraphX
  • SparkR

Let’s learn about the key features offered by each of these components –

  • Apache Spark Core
  • It is responsible for I/O functionalities
  • It is significant in programming and managing the Spark cluster
  • Fault recovery
  • Task dispatching
  • It overcomes the MapReduce snag with in-memory computation
  • Apache Spark SQL
  • Cost based optimizer
  • Mid query fault tolerance
  • Complete compatibility with existing Hive data
  • DataFrames and SQL offer way to access distinct data sources
  • Ability to carry structured data within Spark programs with the help of SQL or familiar data frame API
  • Apache Spark streaming
  • Enables scalable, fault-tolerant stream processing
  • Ability to operate using various algorithms
  • Ability to access data from sources, such as Flume, TCP socket, Kafka, etc.
  • Apache Spark MLlib (Machine Learning Library)
  • It has machine learning libraries
  • It is more user-friendly
  • Apache Spark GraphX
  • It extends Spark RDD
  • It has possibilities of clustering, classification, searching, traversal, and pathfinding
  • Apache SparkR
  • It contains DataFrame
  • It offers software facilities for data manipulation, calculation, and graphical display
  • It provides light-weight frontend

Apache Spark implementation strengthens the available Bigdata tool for analysis instead of reinventing the wheel. The above components of Apache Spark make the framework more popular and this is why programmers find it as a common platform for distinct types of data processing. All these features, components, and differences make Apache Spark a better option over Hadoop for professionals. In case you have any reason to deny these facts, feel free to comment below.

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