Difference Between Hadoop and Apache Spark

Hadoop: It is a collection of open-source software utilities that facilitate using a network of many computers to solve problems involving massive amounts of data and computation. It provides a software framework for distributed storage and processing of big data using the MapReduce programming model. 
Hadoop is built in Java, and accessible through many programming languages, for writing MapReduce code, including Python, through a Thrift client. It’s available either open-source through the Apache distribution, or through vendors such as Cloudera (the largest Hadoop vendor by size and scope), MapR, or HortonWorks. 

Apache Spark: It is an open-source distributed general-purpose cluster-computing framework. Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. 
Spark is structured around Spark Core, the engine that drives the scheduling, optimizations, and RDD abstraction, as well as connects Spark to the correct filesystem (HDFS, S3, RDBMS, or Elasticsearch). There are several libraries that operate on top of Spark Core, including Spark SQL, which allows you to run SQL-like commands on distributed data sets, MLLib for machine learning, GraphX for graph problems, and streaming which allows for the input of continually streaming log data. 

 

Hadoop-vs-Apache-Spark

Below is a table of differences between Hadoop and Apache Spark: 



 

 

Features Hadoop Apache Spark
Data Processing Apache Hadoop provides batch processing Apache Spark provides both batch processing and stream processing
Memory usage Spark uses large amounts of RAM Hadoop is disk-bound
Security Better security features It security is currently in its infancy
Fault Tolerance Replication is used for fault tolerance RDD and various data storage models are used for fault tolereance
Graph Processing Algorithms like PageRank is used Spark comes with a graph computation library called GraphX
Ease of Use Difficult to use Easier to use
Real-time data processing It fails when it comes to real-time data processing It can process real-time data
Speed Hadoop’s MapReduce model reads and writes from a disk, thus slow down the processing speed Spark reduces the number of read/write cycles to disk and store intermediate data in-memory, hence faster-processing speed.
Latency It is high latency computing framework It is a low latency computing and can process data interactively

 

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