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.
What is Hadoop Technology?
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.
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.
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.
What is Hadoop Architecture?
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. Now in this Hadoop for beginners tutorial for beginners, we will discuss different components of Hadoop in detail.
There are three most important Apache Hadoop Components. In this Hadoop tutorial, you will learn what is HDFS, what is Hadoop MapReduce and what is Yarn Hadoop. Let us discuss them one by one-
What is HDFS?
Hadoop HDFS or Hadoop Distributed File System is a distributed file system which provides storage in Hadoop in a distributed fashion.
In Hadoop Architecture on the master node, a daemon called namenode run for HDFS. On all the slaves a daemon called datanode run for HDFS. Hence slaves are also called as datanode. Namenode stores meta-data and manages the datanodes. On the other hand, Datanodes stores the data and do the actual task.
HDFS is a highly fault-tolerant, distributed, reliable and scalable file system for data storage. First Follow this guide to learn more about features of HDFS and then proceed further with the Hadoop tutorial.
HDFS is developed to handle huge volumes of data. The file size expected is in the range of GBs to TBs. A file is split up into blocks (default 128 MB) and stored distributedly across multiple machines. These blocks replicate as per the replication factor. After replication, it stored at different nodes. This handles the failure of a node in the cluster. So if there is a file of 640 MB, it breaks down into 5 blocks of 128 MB each (if we use the default value).
What is MapReduce?
In this Hadoop Basics Tutorial, now its time to understand one of the most important pillars if Hadoop, i.e. Hadoop MapReduce. The Hadoop MapReduce is a programming model. As it is designed for large volumes of data in parallel by dividing the work into a set of independent tasks. MapReduce is the heart of Hadoop, it moves computation close to the data. As a movement of a huge volume of data will be very costly. It allows massive scalability across hundreds or thousands of servers in a Hadoop cluster.
Hence, Hadoop MapReduce is a framework for distributed processing of huge volumes of data set over a cluster of nodes. As data stores in a distributed manner in HDFS. It provides the way to Map–Reduce to perform parallel processing.
What is YARN Hadoop?
YARN – Yet Another Resource Negotiator is the resource management layer of Hadoop. In the multi-node cluster, as it becomes very complex to manage/allocate/release the resources (CPU, memory, disk). Hadoop Yarn manages the resources quite efficiently. It allocates the same on request from any application.
On the master node, the ResourceManager daemon runs on the YARN then for all the slave nodes NodeManager daemon runs.
Learn the differences between two resource manager Yarn vs. Apache Mesos. Next topic in the Big Data Hadoop for beginners is a very important part of Hadoop i.e. Hadoop Daemons