Apache Hadoop Article for Beginners
Objective
Hadoop article is a comprehensive guide on Big Data Hadoop that covers what is Hadoop, what is the need of Apache Hadoop, why Apache Hadoop is most popular, How Apache Hadoop works?
Apache Hadoop is an open source, Scalable, and Fault-tolerant framework written in Java. It efficiently processes large volumes of data on a cluster of commodity hardware. Hadoop is not only a storage system but is a platform for large data storage as well as processing. This Big Data Hadoop tutorial provides a thorough Hadoop introduction.
We will also learn in this Hadoop tutorial about Hadoop architecture, Hadoop daemons, different flavors of Hadoop. At last, we will cover the introduction of Hadoop components like HDFS, MapReduce, Yarn, etc.
What is Hadoop?
Hadoop is an open-source tool from the ASF – Apache Software Foundation. Open source project means it is freely available and we can even change its source code as per the requirements. If certain functionality does not fulfill your need then you can change it according to your need. Most of Hadoop code is written by Yahoo, IBM, Facebook, Cloudera.
It provides an efficient framework for running jobs on multiple nodes of clusters. Cluster means a group of systems connected via LAN. Apache Hadoop provides parallel processing of data as it works on multiple machines simultaneously.
By getting inspiration from Google, which has written a paper about the technologies. It is using technologies like Map-Reduce programming model as well as its file system (GFS). As Hadoop was originally written for the Nutch search engine project. When Doug Cutting and his team were working on it, very soon Hadoop became a top-level project due to its huge popularity.
Apache Hadoop is an open source framework written in Java. The basic Hadoop programming language is Java, but this does not mean you can code only in Java. You can code in C, C++, Perl, Python, ruby etc. You can code the Hadoop framework in any language but it will be more good to code in java as you will have lower level control of the code.
Big Data and Hadoop efficiently process large volumes of data on a cluster of commodity hardware. Hadoop is for processing huge volume of data. Commodity hardware is the low-end hardware, they are cheap devices which are very economical. Hence, Hadoop is very economic.
Hadoop can be set up on a single machine (pseudo-distributed mode, but it shows its real power with a cluster of machines. We can scale it to thousand nodes on the fly ie, without any downtime. Therefore, we need not make any system down to add more systems in the cluster. Follow this guide to learn Hadoop installation on a multi-node cluster.
Hadoop consists of three key parts –
Hadoop Distributed File System (HDFS) – It is the storage layer of Hadoop.
Map-Reduce – It is the data processing layer of Hadoop.
YARN – It is the resource management layer of Hadoop.
In this Hadoop Article for beginners we will all these three in detail, but first let's discuss the significance of Hadoop.
Why Hadoop?
Let us now understand in this Hadoop tutorial that why Big Data Hadoop is very popular, why Hadoop capture more than 90% of big data market.
Apache Hadoop is not only a storage system but is a platform for data storage as well as processing. It is scalable (as we can add more nodes on the fly), Fault-tolerant (Even if nodes go down, data processed by another node).
Following characteristics of Hadoop make it a unique platform:
Flexibility to store and mine any type of data whether it is structured, semi-structured or unstructured. It is not bounded by a single schema.
Excels at processing data of complex nature. Its scale-out architecture divides workloads across many nodes. Another added advantage is that its flexible file-system eliminates ETL bottlenecks.
Scales economically, as discussed it can deploy on commodity hardware. Apart from this its open-source nature guards against vendor lock.
Learn Hadoop features in detail.
What is Hadoop Architecture?
After understanding what is Apache Hadoop, let us now understand the Big Data Hadoop Architecture in detail in this Hadoop tutorial.
Hadoop works in master-slave fashion. There is a master node and there are n numbers of slave nodes where n can be 1000s. Master manages, maintains and monitors the slaves while slaves are the actual worker nodes. In Hadoop architecture, the Master should deploy on good configuration hardware, not just commodity hardware. As it is the centerpiece of Hadoop cluster.
Master stores the metadata (data about data) while slaves are the nodes which store the data. Distributedly data stores in the cluster. The client connects to master node to perform any task.
Hadoop Components
There are three most important Apache Hadoop Components. In this Hadoop article, you will learn what is HDFS, what is Hadoop MapReduce and what is Yarn Hadoop.
Hadoop HDFS or Hadoop Distributed File System
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