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10 Frequently-asked Hadoop Interview Questions with Answers

10 Frequently-asked Hadoop Interview Questions with Answers

A substantial part of the Apache project, Hadoop is an open source, Java-based programming software framework that is used for storing data and running applications on different clusters of commodity hardware. Be it any kind of data, Hadoop acts as a massive storage unit backed by gargantuan processing power and an ability to tackle virtually countless tasks and jobs, simultaneously.

In this blogpost, we are going to discuss top 10 Hadoop interview questions – cracking these questions may help you bag the sexiest job of this decade.

What are the components of Hadoop?

There are 3 layers in Hadoop and they are as follows:

  • Storage layer (HDFS) – Also known as Hadoop Distributed File System, HDFS is responsible for storing various forms of data as blocks of information. It includes NameNode and DataNode.
  • Batch processing engine (MapReduce) For parallel processing of large data sets across a standard Hadoop cluster, MapReduce is the key.
  • Resource management layer (YARN) Yet Another Resource Negotiator is the powerful processing framework in Hadoop system that keeps a check on the resources.

Why is Hadoop streaming?

Hadoop distribution includes a generic application programming interface for drawing MapReduce jobs in programming languages like Ruby, Python, Perl, etc. and this is known as Hadoop streaming.

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What are the different modes to run Hadoop?

  • Local (standalone) Mode
  • Pseudo-Distributed Mode
  • Fully-Distributed Mode

How to restart Namenode?

Begin by clicking on stop-all.sh and then on start-all.sh

OR

Write sudo hdfs (then press enter), su-hdfs (then press enter), /etc/init.d/ha (then press enter) and finally /etc/init.d/Hadoop-0.20-name node start (then press enter).

How can you copy files between HDFS clusters?

Use multiple nodes and the distcp command to ensure smooth copying of files between HDFS clusters.

What do you mean by speculative execution in Hadoop?

In case, a node executes a task slower, the master node has the ability to start the same task on another node. As a result, the task that finishes off first will be accepted and the other one will be rejected. This entire procedure is known as “speculative execution”.

What is “WAL” in HBase?

Here, WAL stands for “Write Ahead Log (WAL)”, which is a file located in every Region Server across the distributed environment. It is mostly used to recover data sets in case of mishaps.

How to do a file system check in HDFS?

FSCK command is your to-go option to do file system check in HDFS. This command is extensively used to block locations or names or check overall health of any files.

Follow

hdfs fsck /dir/hadoop-test -files -blocks –locations

What sets apart an InputSplit from a Block?

A block divides the data, physically without taking into account the logical equations. This signifies you can posses a record that originated in one block and stretches over to another. On the other hand, InputSplit includes the logical boundaries of records, which are crucial too.

Why should you use Storm for Real-Time Processing?

  • Easy to operate simple operating system makes it easy
  • Fast processing it can process around 100 messages per second per node
  • Fault detection it can easily detect faults and restarts functional attributes
  • Scores high on reliability expect execution of each data unit at least for once
  • High scalability it operates throughout clusters of machines


The article has been sourced from
– www.besthadooptraining.in/blog/top-100-hadoop-interview-questions

 

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Top 6 Big Data Trends for 2018

Big data is expanding, and by next year almost a majority of businesses will be attracted towards the brighter prospect of this cutting edge technology. Even this year saw an enormous increase in volume, variety, velocity of data, which assures that the next year will witness more data, more numbers.

 
Top 6 Big Data Trends for 2018
 

Data science pundits have predicted some of the leading trends that would be in the forefront in the big data revolution 2018. Come, let’s take a look:

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The Impact of Big Data on Marketing

The Impact of Big Data on Marketing

In marketing, the analysis of data is a highly established one but the marketers nowadays have a massive amount of public and proprietary data about the preferences, usage, and behavior of a customer. The term ‘big data’ points out to this data explosion and the capability to use the data insights to make informed decisions. Understanding the potential of big data presents various technical challenges but it also needs executive talent devoted to applying the solutions of big data. Today, the marketers are widely embracing big data and are confident in their use of analytics tools and techniques. Let us learn about the ways in which Big data and analytics can improve the marketing efforts of various businesses around the around.

Locating Prospective Customers

Previously, marketers had to frequently make guesses as to which sector of population comes under their ideal market segment but this is no longer the scenario today. The companies can exactly see who is buying and even extract more details about them with the help of big data. The other details include which buttons they generally click while on a website, which websites they visit frequently, and which social media channels they utilize.

Tracking Impact and ROI

Many retailers have introduced loyalty card systems that track the purchases of a customer, but these systems can also track which promotions and incentives are most effective in encouraging a group of customers or a single customer to make another purchase.

Handling Marketing Budgets

Because big data allows companies to optimize and monitor their marketing campaigns for performance, this implies they can allocate their budget for marketing for the highest return-on-investment (ROI).

Personalizing Offers in Real-Time

Marketers can personalize their offers to customers in real time with the combination of big data and machine learning algorithms. Think about the Amazon’s “customers also bought” section or the recommended list of TV shows and movies from Netflix. The organizations can personalize what promotions and products a particular customer views, even down to sending personalized offers and coupons to the mobile phone of a customer when he walks into a physical location. The role of Personalized Merchandising in the ecommerce industry will continue to increase in the years to come.

Improvement in Market Research

Companies can conduct quantitative and qualitative market research much more inexpensively and quickly than ever before. The tools for online survey mean that customer feedback and focus groups are inexpensive and easy to implement, and data analytics make the results easier to take action.

Prediction of Buyer Behavior and Sales

For the past several years, sales teams, in order to rate their hottest leads, have made use of lead scoring. But, with the help of predictive analytics, a model can be generated and it can successfully predict sales and buyer behavior.

 

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Enhanced Content Marketing

Previously, the return-on-investment for a blog post used to be highly difficult to measure. But, with the help of big data and analytics, the marketers can effortlessly analyze which pieces of content are highly effective at moving leads via a sales and marketing funnel. Even a small firm can afford to use tools for implementing content scoring which can highlight the content pieces that are highly responsible for closing sales.

Optimize Customer Engagement

Data can provide more information about your customers which includes who they are, what they want, where they are, how often they purchase on your site, and how, when they prefer to be contacted, and various other major factors. The organizations can also examine how users interact not only with their website, but also their physical store to enhance the experience of the user.

Tracking Competitors

New tools for social monitoring have made it easy to gather and examine data about the competitors and their efforts regarding marketing as well. The organizations that can utilize this data will have a distinct competitive advantage.

Managing Reputation

With the help of big data, organizations can monitor their brand mentions very easily across different social channels and websites to locate unfiltered testimonials, reviews, and opinions about their company and products. The savviest can also utilize social media to offer service to the customers and create a trustworthy brand presence.

Marketing Optimization

It is quite difficult to track direct ROI and impact with traditional advertising. But, big data can help organizations to make optimal marketing buys across various channels and to optimize their marketing efforts continuously through analysis, measurement, and testing.

What is Needed for Big Data?

At this point, talent and leadership are the major things that big data needs. In most of the companies, the marketing teams don’t have the right talent in place to leverage analytics and data. Apart from people who possess analytical skills to understand the capability of big data and where to use it, companies require data scientists who can extract meaningful insights from data and the technologists who can develop include new technologies. Due to this, there is a high demand for experienced analytics talent today.

Big Data Limitations for Marketing

In spite of all the promise, there exist certain limits to the usefulness of big data analytics in its present state. Among them, the major one is the major one is the analytics tools’ and techniques’ complex “black box” nature which makes it hard to trust and interpret the output of the approaches of big data and to assure others of the accuracy and value of the insights generated by the tools. The difficulty of gathering and understanding data also limits the capability of marketing companies to more fully leverage big data. Beyond this, the marketers are identifying many hurdles to expanding their utilization of big data tools and they include lack of sufficient technology investment, the inability of senior team members to leverage big data tools for decision-making, and the lack of credible tools for measuring effectiveness.

Conclusion

Cloud computing is also playing a major role in marketing with the Cloud Marketing process. Cloud Marketing is a process that outlines the efforts of a company to market their services and goods online via integrated digital experiences. Once the data analytics tools become available and accessible to even the smallest businesses, there will be a much higher impact of big data on the marketing sector as there will be much broader utilization of data analytics. This can only be a boon as organizations enhance their marketing and reach their customers in innovative and new ways.

This article was produced by Savaram Ravindra, a content contributor at Mindmajix and not by the editorial team of DexLab Analytics, a leading Hadoop training institute in Gurgaon.

 

Author’s Bio: Savaram Ravindra was born and raised in Hyderabad, popularly known as the ‘City of Pearls’. He is presently working at Mindmajix.com. His previous professional experience includes Programmer Analyst at Cognizant Technology Solutions. He holds a Masters degree in Nanotechnology from VIT University. He can be contacted at savaramravindra4@gmail.com. Connect with him also on LinkedIn and Twitter.

 

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How to Secure Big data While Harnessing Its Big Power

The term Big Data stands for data that is humongous. Large volumes of data are being churned out every day to meet business needs.

 
How to Secure Big data While Harnessing Its Big Power
 

Business analytics is the bedrock of an organization. It uses data for proper analysis of business objectives, later on which helps in making better decisions and future profit generation. Also, it aids in determining the actual reasons of failures, re-evaluating risk portfolios, and detecting undergoing fraudulent activities before they swell up to affect business operations.

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Big Data is the Magic Wand to Cure Healthcare Industry Hiccups

Big Data is the Magic Wand to Cure Healthcare Industry Hiccups

Spurred by advanced Analytics and Big Data technologies, Healthcare industry is going towards a major transformation, of course for the good! The catalyst here is none but our very own, our most favorite Big Data – it is robustly opening all the doors of health and medical science, and the possibilities seem endless.

Electronic Health Records have been around for sometime – numerous systems of variable reliability have been designed to ensure data is more easily accessible as well as transferable between the healthcare professionals, institutions and whatever it is for better patients’ care. With Big Data, scientists are coming up with improved sophisticated methods of incorporating the derived information with the data from innumerable number of health-related sources. The main objective is to make the best use of the relevant information in consultation with the doctors and patients to serve in the best way possible.

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Nowadays, plenty of veritable companies provide systems which not only help in providing the doctors a detailed study of a patient’s medical history but also supply with data that can be used largely for fine treatment purposes. Highlighting correlations between different medical conditions inaccessible before, sparing insights into how these conditions may be influenced by other factors, like treatment methods or in which part of the world they are taking place are some improvements to be witnessed now.

As estimated, 75% of healthcare data is generated from unstructured sources like clinical notes, laboratory tests, emails, telematics, digital devices, imaging and third party sources. This data revolution is brought to you by Big Data, and this is how you can derive the best of its benefits:

Reduce fraud, abuse and waste

We all know how fraud, abuse and waste have been spiking healthcare costs, thanks to data science, the tides are changing now. To ascertain abuse and fraud, insurers require the expertise to analyze large unstructured datasets related to historical claims using machine learning algorithms.

Improve outcomes, embrace Predictive Analysis

Predictive Modeling is helping the health world in detecting the early signs of life threatening diseases, like sepsis. The availability of a vast pool of patients’ data means Predictive Analytics would find not only similar symptoms but also will curate a similar response to a specific medication.

Healthcare Internet of Things

The Internet of Things (IoT) is the aggregation of the increasing number of smart, interconnected, technology-efficient devices and sensors that share data over the internet. In healthcare, IoT refers to the devices that monitor almost all kinds of patient behavior, right from blood pressure to ECG. As per statistics, spending on healthcare IoT could cross $120 billion mark in the coming four years and the possibilities are quite high.

Minimum costs but better patients’ recovery rates

Through data convergence, stream processing and application agility, full-scale digital transformation is now possible in the medical world. Improving patients’ diagnosis is a new milestone achieved in the field of medicines and it has only been possible due to advancement in data science.

 

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Hadoop 2017: The Survivor and Not the Casualty

Hadoop 2017: The Survivor and Not the Casualty

 

Most people decipher – Hadoop and Big Data are the two sides of the same coin. Adding the fascinating word to your resume leads to better opportunities and higher pay structure. But what the future holds for Hadoop? Is it dismal or encouraging?

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Attributes of Effective Data Monetization Strategy

Attributes of Effective Data Monetization Strategy

The saying goes – ‘necessity is the mother of all inventions’ and with the advent of globalization we have witnessed this aphorism in its sincerest form. A new wave of competition and profit generation owing to the advent of the internet, within the labyrinth of our society has led to the creation of Data at a scale previously unthinkable. To capture the essence of this huge amount of data, a new term Big Data, was coined which meant extremely large data sets, which are to be analyzed to reveal patterns that lie within.

Today, technology has become the backbone of the society and data is its vertebrae. The technological boom began and became common around year 2000; this is when data monetization became apparent.

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To simplify, data monetization is the act of generating revenue by exchanging, processing and analysis of data. Processing and analyzing means extraction of value from a particular set of data; eventually this value is to be interpreted to make decisions.

The need for data analysis is apparent since the digital universe is expected to grow 50-fold in terms of data by 2020, yet today only about 1% of the data is analyzed.

To capitalize on data monetization, we can employ the following approaches:

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  • An improvement in internal business processes – To locate synergy between different results, one result may provide some information, but coupled with another piece of result obtained, the synergistic outcome may be far more valuable.
  • Wrapping information around core products and services – This can be accomplished through understanding the target customer (analysis of their online presence can yield valuable information), and many companies are already indulging in these practices.
  • Trade of information to existing markets – This can often lead to be the most profitable of the three approaches, depending on the information, which it possesses.
  • Developing a technological structure – A technological infrastructure, capable enough to churn a real time data and provide real time results would be a boon to any business.


Already 70% of the large institutions purchase external data and monetization of the informatio
n asset is still in its infant stage. According to a study performed by Gartner, Data Monetization will be performed by 30% of the companies or more and in a survey conducted by IBM, data monetization was found to be among top 5 priorities of an organization.

The above clearly implies the upward trajectory growth in the near future in this industry, and with the application of the above-mentioned approaches, an effective strategy can be implemented by any organization hoping to be a part of the Data Monetization phenomenon.

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Tigers will be safe in the hands of Big Data Analytics

Once again, good news is in the air for our very own ‘Big Cats’. The very recent reports on Tiger Census have proudly announced the incredible rise in the number from 1,706 to 2, 226 since 2010, when the counting started.

 
Tigers-will-be-safe-in-the-hands-of-Big-Data-Analytics
 

The previous years have seen the major downfall in the number owing to reasons like poaching, environmental degradation, dwindling habitats and of course man- nature conflict . But in contrast, the combined efforts put forwarded by local communities, conservationists and the Government has resulted in the upliftment, as stated by Marco Lambertini, Director General of WWF International.

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How MTV and Nickelodeon Use Real-Time Big Data Analytics To Improve Customer Experience

Viacom the owner of household brands such as Comedy Central, Nickelodeon and MTV, is one of the largest media companies in the world, delivering more than 170 cable, broadcast and online networks in around 160 countries.

How MTV and Nickelodeon Use Real-Time Big Data Analytics To Improve Customer Experience

Monitoring of the digital networks, which are used to pump their content into millions of homes, gives them access to a huge amount of data, on how both their systems and their audiences behave.

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