Hadoop: The next big thing in the Programming world

Hadoop is a group of open-source software services that is used to solve problems of data and computation. This is all done by using web network of many computers to solve complex problems. It facilitates enormous storage for different data, fast processing power and can handle simultaneous tasks at an ease. It uses MapReduce Programming model to process such large amount of data. It was initially designed for computer clusters which, in today’s world, are still in use. But gradually it has also started being used for high-end hardware. In this article, we are going to talk about the Hadoop: The next big thing in the Programming world

How Hadoop was born?

The late 1900s and early 2000s was the time period of World Wide Web. It was the time when search engines and indexes were developed to extract required information from the internet. Unlike today, search results were returned by person rather than an algorithm. But as development grew in programming, web-crawlers came into the picture. They reduced the human workforce and provided faster search results. These advancements in programming gave birth to companies like Yahoo, AltaVista etc.

In 1999, Apache Software Foundation (ASF) was formed as a Non-Profit Organisation. Then in 2002, an open-source web search engine called Nutch was created by Doug Cutting and Mike Cafarella. Their aim was to make web search results faster by using different computers to distribute data and calculations. It was then in the year 2006, Cutting joined Yahoo and took Nutch along with him. On the opposite side, another search engine project, known as Google was also in development.

Finally in 2008, Nutch project was divided and Hadoop was born. Nutch remained as a web crawler portion and Hadoop became the distributed and processing portion. Later in that year, Yahoo launched Hadoop as an open-source project. A Hadoop-based start-up named Cloudera was also incorporated in 2008. At present, Hadoop’s architecture and ecosystem of its technology are managed by Apache Software Foundation(ASF), a non-profit global community of software developers.

Why Hadoop became so important?

Managing Big Data

The progamming platform Hadoop has the ability to store and process large amount of data of any kind quickly and effectively. As the data volumes and varieties are constantly increasing, Hadoop will come in handy, especially considering social media and Internet of Things (IoT).

Computing Power

The whole program of Hadoop uses MapReduce Programming model to split files into large blocks and distributes them across different nodes in a network of advanced computers. Packaged nodes are then transferred to process the data alongside.

Highly Scale-able

It is designed in such a way that Hadoop is highly scale-able, as one can easily grow the system to process more data by adding nodes. Unlike Relational Database Management Systems (RDBMS), Hadoop can run applications on thousands of nodes effectively.

Economical

It can economical storage solution for large amount of data. While the traditional RDBMS is worthless in terms of data storage, Hadoop stands out really good in this genre. Moreover, Hadoop is an open source framework which is free and easily available.

Flexibility

The interface of Hadoop lets the developer to access new data sources easily, work out and preprocess it before storing it. One can stockpile large amount of data and preplan how to use it later. Also it can be used for many purposes such as data warehousing, log processing, fraud detection and market campaign analysis.

Accelerated Processing

It is to be noted that Hadoop is blazingly fast when it comes to processing of large amount of data. Its unique storage method, known as Hadoop Distributed File System (HDFS), is a distributed file system which stores data on commodity machines. It basically maps the data wherever it is located on the cluster.

What are the demerits of using Hadoop?

Data Protection

Its security model is confined by default due to sheer complexity. If data management is in the amateur hands, your data can be at risk. Though new tools and technologies are surfacing, fragmented data security is still an issue.

Required Skill Set

The skill set required in MapReduce Programming Model is still very less today. Entry-level programmers are difficult to find who have sufficient Java skills to be dynamic with MapReduce. Rather SQL programmers are easier to find than MapReduce programmers. Hadoop is a combination of art and science skills and doesn’t demand much knowledge of hardware, operating system and its kernel settings.

Small data is not welcomed

It lacks the ability to efficiently support small data due to its high capacity design. While in some cases, big businesses are suited for big data needs, but they also need small data processing. Hence it is not advisable to be used with businesses having small data chunks.

Stability issues

Open Source software have stability issues hence Hadoop also shares some of it. To prevent it some businesses are advised to run the latest stable version, as all the bugs are removed from it from time to time. They can also run it under a third-party vendor rigged to handle such issues.

No full-featured data management

 

It doesn’t have fully fledged data management and governance. It also lacks full feature tools, easy to use for governance, data cleansing and metadata. It doesn’t have tools for standardization and data quality.

What is Hadoop’s present scenario?

in the present scenario, Hadoop’s promising features such as large storage capability, low-cost and high processing power has drawn attention of many businesses. Still, a question remains in all our minds that, How Hadoop can aid us with big analytics and data?

TWDI

TWDI is an organization which provides individual and teams an overall research data and portfolio about all things. A report published by TWDI believes that Hadoop operation will become mainstream in the coming years. Moreover Hadoop will assist as an whole enterprise rather than a small commodity with limited number of users.

The Hadoop Ecosystem

Bloor Group examines the Hadoop ecosystem on the evolution and deployment side. It consists of a detailed history on how to select a distribution according to our needs. Their research shows that, at current of approval, Hadoop and its ecosystem will be effective in the area of BI applications and analytics for the enterprise. This will dominate about one-third of the enterprise IT in the coming years.

Hadoop’s Self Service Model

Today, Hadoop’s scenario is very different from where it begun initially. Hadoop ecosystem has all the tools today such as SQL, NoSQL, Streaming Event Processing engine and Machine Learning. These self service tools like SAS Data Preparation allows the non-technical users to individually access and assemble data for analysis. It also enables users to respond faster to business needs.

But what the dependencies are and what functions can be made self-service?

It is a snap to plan for the frequently asked functions when it comes to analytics of just the self service. The target here is not to remove the scope but assure the returns are greater than the investment to elevate it. Self Service is generally hard to be established due to many reasons such as data security, data quality, organizational culture, data governance and arrangement of all the required datasets. It is also difficult to understand that self-service is not a bygone feature delivered in a well groomed business.

Overview of SAS and Hadoop

When you use SAS and Hadoop together, you associate key strengths with the power of analytics. It includes compute resources, commodity based storage and large-scale data processing. Hadoop can be powered up using SAS. Like other data sources, data stored in Hadoop can be apparently consumed by SAS. Moreover the tools of SAS can also be used with Hadoop. The data stored in Hadoop can be accessed by SAS and SAS can also aid in managing your Hadoop data.

The power of SAS analytics is drawn-out to Hadoop. Even before the term big data was conceived, SAS implemented complex analytical processes to bigger data volumes. Each SAS technology contributes from executing analytical models in a Hadoop cluster to managing Hadoop data. Along with the variety of functionality, SAS executes Hadoop data using variable methods so that an appropriate business problem can be resolved in an excellent way.

How is Hadoop seen as a next big data platform?

More data is only beneficial only if the data is smart data. SAS is a powerful adulation to Hadoop. SAS arrange everything you need to get as much value as you want from your large data.

Some consider Hadoop Distributed File System (HDFS) to be a data store because of lack of POSIX compliance, but it does administer Java application programming interface and shell commands. Hadoop consists of two parts i.e. HDFS and MapReduce. HDFS is used for storing the data and MapReduce is used for the data processing.

HDFS has five key features as follows:

  1. Data Node
  2. Name Node
  3. Secondary Name Node
  4. Task Tracker
  5. Job Tracker

Name Node, Secondary Name Node and Job Tracker are Master Services/Nodes/Daemons and Data Node, Task Tracker are Slave Services.

Data Node

A data node stores the data as small blocks. This is also called slave node as it stores the actual data into HDFS which is answerable for the client to read and write. These are known as slave daemons. Every Data node delivers a message, known as Heartbeat message, to the Name node every 3 seconds and transmits that it is alive.

Name Node

HDFS consists of only one node which is known as Master node that manage the file system, has the meta data, track the files and has the whole data in it. To be precise Name node stores the details of the locations and No of blocks at where the replications and at what data node the data is stored.

Secondary Name Node

It works at one of the checkpoints of the file system metadata which consists in the Name Node. Hence it is also known as checkpoint node. It assists as a helper node for the Name Node.

Task Tracker

It works as a Slave Node for the Job Tracker. It will receive the task from the Job Tracker and receives code from it. Further, task tracker will receive the code and apply on the file. This process of applying the code on the file is known as Mapper.

Job Tracker

Job Tracker comes really useful in data processing. Job Tracker receives code from the MapReduce execution from the client. It communicates with the Name node to know about the location of the data. Name node then provides the metadata to Job Tracker.

What is a recommendation engine in Hadoop?

One of the most favored interpretive uses by some of Hadoop’s greatest adopters is for web-based recommendation systems. Linkedin- jobs you may be interested in, Facebook- people you may know, Ebay,Netflix, Hulu- items you may want. These kind of systems evaluate large amount of data in real-time to quickly conclude preferences before customers leave the webpage.

The recommendation engine needs to be validated for accuracy and precision. Accuracy can be explained as the ratio between the true positives and true negatives divide by all positive results. Meanwhile, Precision can be defined as the ratio between true positives and the sum of true positives and false positives.

Positives and Negatives are decided by sentiment investigation, by classifying the words used by clients. Recommendation engine provides feature to understand a person’s taste and discover new, desirable content for them. A large amount of data is accessible on the internet in the form of ranks, complain, remarks, ratings, feedback and comments.

How Hadoop actually works?

The platform of Hadoop works with the help of three main components namely HDFS, MapReduce and Yarn.

HDFS

HDFS is the storage component of Hadoop. It stores data in a diversified and distributed manner. The file is distributed into a number of blocks which transmits across the cluster of commodity hardware.

MapReduce

MapReduce Programming model is used to process such large amount of data. It was initially designed for computer clusters which, in today’s world, are still in use. But gradually it has also started being used for high-end hardware.

Yarn

Yet Another Resource Manager provides resource management for Hadoop. It consists of two running daemons. First one is Node Manager on the slave machines and another one is the Resource manager on the master node. Yarn conducts the allotment of the resources among various slave competing for it.

Conclusion

The world is developing the way it is performing currently and Big-data is playing an important role in it. Hadoop is a platform that makes an engineer’s life easy while functioning on large sets of data. There are improvements on all the fronts and Hadoop can become the next big thing in the programming world as the need of Big Data arising on daily basis by the multi-national corporations.

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