Guest blog post by Durgesh Kaushik
Named after a kid’s toy elephant and initially recognized as a technical problem, today it drives a market that’s expected to be worth $50 billion by 2020. It is the most talked about technology since its inception as it allows some of the world’s largest companies to store and process data sets on clusters of commodity hardware.
Hadoop is the adorable little yellow elephant with qualities that work double its size! It’s an open-source software framework used for storing and processing big data in a distributed manner on large clusters of hardware. Known for its ability to handle huge and any kind of data, this charmer is known for other reasons as well.
Big Data is a buzzword used to describe data that is large, both structured and unstructured. The term big data, may refer to the technology that an organization requires to handle the large amounts of data and storage facilities. The term big data is believed to have originated with web search companies who needed to query very large distributed aggregations of loosely-structured data. Big Data today has huge prospects in different companies across different fields.
The role of big data in Hadoop signifies that Hadoop can take up the challenge of handling huge amounts of data. With Hadoop, no data is big and helps in efficiently storing and processing data.
You will be surprised to know about the growing popularity of Big Data and how it has been fairing this year. Furthermore, much is said about Hadoop 2.0 and how competitive it has got in comparison to the previous version. So how has the yellow elephant grown in terms of its potential?
Hadoop 2.0 is an endeavor to create a new framework for the way big data can be stored, mined and processed. It allows the creation of new data methodologies within Hadoop, which wasn’t possible earlier due to its architectural limitations. To know more about setting a single node cluster, this blog will help you understand further. ‘Setting up a single node cluster in 15 minutes!’
The new Hadoop 2.0 architecture executes better performance in comparison to the previous version with higher availability.
Let’s take an example of a house construction. Tremendous effort goes into building a house. With bricks, cement and a good share of planning, the procedure of establishing a house begins! Discard the planning aspect from this and what do you get in the bargain? To imagine your house without a well-planned architecture is to imagine it without a proper entry and an exit.
Likewise, to understand the concept of Hadoop, grasping the understanding of the architecture is crucial. The Hadoop Distributed File System (HDFS) is designed to store very large data sets reliably, and to stream those data sets at high bandwidth to user applications. In a large cluster, thousands of servers host directly attached storage and execute user application tasks.
Hadoop consists of core components that help the yellow toy in speeding up better!
Role of Hadoop Components
Your system consists of various organs that have an important role to play without which your body would not function and would just remain lifeless. Similarly, Hadoop alone cannot do wonders. It requires its cadre to support it for better performance. This is where PIG, Hive, Scoop, MapR and HBase come into play.
PIG- A platform used for manipulating data stored in HDFS and it consists of a compiler for MapReduce programs and a high-level language called PIG Latin.
Hive- A data warehousing and SQL like query language that presents data in the form of tables. Hive also supports Associative Arrays, Lists, Structs, and serialized and de-serialized API is used to move data in and out of tables. Hive has a set of data models as well.
MapReduce- A software programme that processes large sets of data. It has been a game-changer in supporting the enormous processing needs of big data. A large data procedure which might take 20 hours of processing time on a centralized relational database system, may only take 3 minutes when distributed across a large Hadoop cluster of commodity servers, all processing in parallel.
Oozie- Oozie is a workflow scheduler system to manage Hadoop jobs. It is a server-based Workflow Engine specialized in running workflow jobs with actions that run Hadoop MapReduce and Pig jobs. Oozie is implemented as a Java Web-Application that runs in a Java Servlet-Container.
Sqoop- Sqoop is a command line interface application for transferring data between relational databases and Hadoop.
YARN- YARN stands out to be one of the key features in the second generation of Hadoop. Initially, described by Apache as a redesigned resource manager, YARN is now characterized as a large-scale, distributed operating system for big data applications.
HBase- HBase is the Hadoop database. It is a distributed, scalable, big data store. HBase is a sub-project of the Apache Hadoop Project and is used to provide real-time read and write access to your big data.
Watch the video for more information on MapReduce Programming!
Hadoop Job Tracker
The Job tracker daemon is a link between your applications and Hadoop. Once the code is submitted to the cluster, the Job Tracker determines the execution plan by determining which files to process, assigns nodes to different tasks, and monitor all tasks as they are running. Read more about the Job Tracker process.
To understand Hadoop better, perceiving the right knowledge of the entire ecosystem will enable you to understand how every component compliments each other.
The very term ecosystem indicates an environment that accommodates an array of components. Likewise, the Hadoop ecosystem comprises components that perform compatible tasks. The Hadoop ecosystem consists of HDFS which is designed to be a scalable and distributed storage system that works closely with MapReduce, whereas MapReduce is a programming model and an associated implementation for processing and generating large data sets.
After glancing through Hadoop, you have enough and more reasons to understand in detail, why is the yellow toy so important.
Reasons to learn Hadoop
There are enough and more reasons as to why you should study Hadoop. It’s a promising career that will open up doors of opportunities.
Switching Careers to Hadoop
Today, we witness a lot of people shifting their careers from Java to Hadoop. A simple reason being, big data is persuading many development team managers to grasp the understanding of Hadoop technology since it’s an important component of Big Data applications.
Do take a peak to know how and why have people favored big data and Hadoop and why should a mainframe professional switch to Big Data and Hadoop?
Role of other applications with Hadoop
Hadoop has made its mark near and far. It’s now a known fact that the use of Hadoop in various fields has had exceptional outcomes and even its combination with the other applications has proven quite constructive, irrespective of it being with Cassandra, Apache Spark, SAP HANA, MongoDB.
Integrating R with Hadoop
Big Data Analytics with R and Hadoop is focused on the techniques of integrating R and Hadoop by various tools such as RHIPE and RHadoop. A powerful data analytics engine can be built, which can process analytics algorithms over a large scale dataset in a scalable manner.
This can be implemented through data analytics operations of R, MapReduce, and HDFS of Hadoop. Hadoop now has become a widely acclaimed analytical tool. For more insights, do read how big data analytics is turning insights to action
Integrating NoSQL with Hadoop
Today, we see an increasing demand for NoSQL skills.The NoSQL community has tried to evolve the meaning of NoSQL to mean “not only SQL,” which refers to a wide variety of databases and data stores that have moved away from the relational data model.
These systems are not only used for Big Data – they support many different use cases that are not necessarily analytical use cases or rely on huge volumes. Hadoop can be also be driven into this category.
Python for Big Data Analytics
Python is a functional and flexible programming language that is powerful enough for experienced programmers to use, but simple enough for beginners as well. Python is a well-developed, stable and fun to use programming language that is adaptable for both small and large development projects. To undertake a big data job, Python training is essential. To know more, read on.
The Cloudera certification is your ticket to become the next best hadoop professional. It is the most sought after certification signifying that you will have your way up the ladder after gaining one.
Today companies are having a difficulty in hiring a Hadoop professional. Cloudera certifies the best specialists who have demonstrated their abilities at the highest level. Do take a peek at why is the Hadoop certification important.
Are you a Hadooper?
Do you have what it takes to be a Hadooper? If you think so, then take a look at whats is in store for you!
Use Cases of Big Data and Hadoop
Big data and Hadoop have several use cases. Hadoop has several business applicationswhile big data plays an important role in the telecom, health care and finance industry. Read on to learn more about its various applications and how Facebook has taken a leap with big data.
P.S Don’t miss out on the 15-minute guide to install Hadoop in the right hand section on top here: http://www.edureka.co/blog/hadoop-tutorial/