What is differences about Apache Hadoop vs Apache Spark
What is Big Data? What size of Data is considered to be big and will be termed as Big Data? We have many relative assumptions for the term Big Data. It is possible that, the amount of data say 50 terabytes can be considered as Big Data for Startup’s but it may not be Big Data for the companies like Google and Facebook. It is because they have infrastructure to store and process this vast amount of data. Apache Hadoop and Apache Spark are both Big Data analytics frameworks they provide some of the most popular tools used to carry out common Big Data-related tasks.
Apache Hadoop Introduction
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer. So delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
Apache Spark Parallel Program Flows
Apache Spark Flows – Apache Spark consists of several purpose-built components as we have discuss at the introduction of apache spark. Let’s see what a typical Spark program looks like. Imagine that a 300 MB log file is stored in a three-node HDFS cluster. Hadoop File System (HDFS) automatically splits the file into 128 MB parts and places each part on a separate node of the cluster.
Apache Spark Component Parallel Processing
Apache Spark consists of several purpose-built components as we have discuss at the introduction of apache spark. Apache spark provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools. These are Apache Spark Component : Spark Core, Spark SQL, Spark Streaming, Spark GraphX, Spark MLlib.
These components make Spark a feature-packed unifying platform: it can be used for many tasks that previously had to be accomplished with several different frameworks. A brief description of each Apache Spark component follows.
Setup And Configure Cluster Node Hadoop Installation
This describes how to setup and configure a cluster-node Hadoop installation so that you can quickly perform simple operations using Hadoop MapReduce and the Hadoop Distributed File System (HDFS).
Apache Spark Parallel Processing Introduction
Apache Spark is usually defined as a fast, general-purpose, distributed computing platform. Apache spark provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.