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Difference between Hadoop, HDFS , Map Reduce, Spark , Spark Streaming & SparkSql


This post explains the difference between the Terminologies ,Technologies & Difference between them – Hadoop, HDFS,  Map Reduce, Spark, Spark Sql & Spark Streaming For a newbie who has started to learn Big Data , the Terminologies sound quite confusing . So lets try to explore each of them and see where they all fit in.  

1. Hadoop

Hadoop is a Collection of  open-source softwares or technologies. It is a Type of Big Data Ecosystem. Hadoop Project was started to facilitate the need of processing the Growing volume of different types of data on a distributed platform.  Traditional ways of data processing were getting inefficient .  Since Hadoop is an Ecosystem, it comprises of different Components & technologies . HDFS , MAP Reduce are two such components in a Hadoop Ecosystem. One main thing to note , The hadoop ecosystem runs over a Cluster of computers.  

HDFS

HDFS is a File System and it is part of the Hadoop ecosystem. HDFS stands for Hadoop Distributed File System. When we say a HDFS file, it means a file stored in HDFS file system. And you can store any types of file e.g csv,, excel, word, text, xml, json , pdf in a HDFS file system. Another point when you store a file in HDFS , HDFS file system internally splits the file into sub-units(called Blocks) and stores those sub-units in the cluster of computers.  

Map Reduce

Map Reduce – It is the Data processing component in a Hadoop ecosystem. Basically Map Reduce is a Framework written in Java. So when you want to process some data (i.e. Files in HDFS system), you need to write a Map Reduce Program in Java. Then compile the Java program into a jar and use the jar to process the data. The outputs of the Map Reduce programs are again written in HDFS file system.

2. Spark

Spark – Spark is also a Parallel Data processing Framework. It is not part of the Hadoop . But it is a different part of the Big Data ecosystem. Spark is an Alternative of Map Reduce (not of Hadoop).  Spark uses RAM to process data which makes it Faster than Map Reduce. Hence now a days, most of the data processing uses Spark – not Map Reduce.

Spark Sql:

SparkSql – It is a sub-module of Spark Framework. Basically it enables you to query Structured Data (e.g. csv, excel, tab delimited data ) using SQL or Dataframe API. So  basically you can write SQL or SQL-type queries to process data (similar to querying a RDBMS table).

Spark Streaming:

Spark Streaming – Spark Streaming is another sub-module of Spark Family. Ttraditional Spark is used to process Batch data i.e.  static files. But imagine if you have a Streaming or flowing data like Twitter tweets or Facebook posts or Sensor data , then you need to use Spark Streaming module. Hope this post clarifies the difference between all these technologies. I will cover them in depth in my subsequent posts.  

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