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Preparing for Hadoop Interview? Here are a Few Predictable Questions


Big Data has been attested as one of the fastest growing technologies of this decade and thus potent enough to produce a large number of jobs. While enterprises across industrial stretch have started building teams, Hadoop technical interview questions could vary from simple definitions to critical case studies. Let’s take a quick glimpse of the most obvious ones.


#1 – What is Big Data?

Big Data refers to such gigantic set of data that has massive potential for mining but cannot be processed as such with traditional tools. However, any data cannot be classified as Big Data; only the set that has high volume, veracity and velocity can be qualified as such. In order to draw meaning from such data, we need to utilize tools such as Hadoop. For that to happen, one needs to undergo a relevant Training in Hadoop or any related software tool.

#2 – What do the four V’s of Big Data denote?

A fitting definition has been put forward by IBM:

  1. Volume: Huge amount of data
  2. Variety: A large variety of data
  3. Veracity: Data that has inherent uncertainty
  4. Velocity: Analysis of streaming data

#3 – How Big Data analysis helps businesses in increasing their revenue?

There are a lot of ways in which businesses can use Big Data analytics to their advantage. For instance, Wal-Mart, the biggest retailer in the world, uses predictive analytics for launching new products on the basis of customer needs and preferences. The who’s who of global businesses – Facebook, LinkedIn, Twitter, Bank of America, and JP Morgan Chase and much more – use the same for boosting their revenue. Businesses and professionals interested in the utilization of the same can choose to learn Hadoop – the most popular tools in this regard.

#4 – Name some companies that use Hadoop?

  • Yahoo (the top contributor with more than 80 percent of its code)
  • Netflix
  • Amazon
  • Hulu
  • Spotify
  • Twitter
  • Amazon


#5 – What is structured and unstructured data?

Structured data refers to such data that can be stored in traditional database systems in the form of columns and rows. On the other hand, unstructured data refers to data that can be stored only partially in traditional database systems.

#6 – On what concept the Hadoop framework works?

HDFS: Hadoop Distributed File System: This is a Java-based file system for reliable storage of large datasets

Hadoop MapReduce: This is Hadoop framework programming paradigm based on Java which provides scalability across various Hadoop clusters.

#7 – List the core components of Hadoop application

  • Hadoop Common
  • HDFS
  • Hadoop MapReduce
  • YARN
  • Data Storage – Pig and Hive
  • Data serialization components: Thrift and Avro

#8 – What is the best Hardware configuration to run Hadoop?

Dual core processor with 4GB or 8GB RAM, with ECC Memory. ECC memory is recommended as non-ECC memory is normally associated with configuration checksum errors.

#9 – What are various common input formats?

  • Text input format – default input format
  • Sequence file format
  • Key value input format

One can develop a deep understanding of key Big Data concepts by opting for Training in Hadoop

#10 – Name some Hadoop tools that are required for working on Big Data.

Some such tools include Hive, HBase and Ambari and much more. Interested individuals should choose to learn Hadoop to get more information on the same.

These were some of the most common yet important Hadoop technical interview questions. A high-level understanding of a few real time case studies could help you sail through.

 For BIG DATA HADOOP Training needs, Visit http://www.zarantech.com/course-list/hadoop/.  Call  515-309-7846 or email  info@zarantech.com.

How ZaranTech fulfill the varying needs of Professionals for Big Data Hadoop?


A collection of complex and large data sets that can be processed using regular database management tools and processing applications known as Big Data. A lot of challenges such as curation, storage, search, capture, sharing, analysis and visualization can be encountered while handling big data. In contrast, using simple programming model Apache Hadoop Software Library is a framework that allows distributed processing of large data sets across computers clusters. It scales up from single servers to thousands of machines where machine offers local computation and storage. Continue reading How ZaranTech fulfill the varying needs of Professionals for Big Data Hadoop?

What is Hadoop good for what it is not?

This article mainly explains about advantages and disadvantages of Hadoop. As the pillar of so many implementations, Hadoop is practically synonymous with big data. Offering dispersed storage, higher scalability, and ultimate performance, many people view this as the standard platform for high volume data infrastructures. To learn more about Hadoop, click on Hadoop Certification.


 Advantages of Hadoop

The following are the advantages of Hadoop:

  • Scalable: Hadoop is a highly scalable storage platform, because it can store and distribute large volume of data sets across hundreds of economical servers that perform in corresponding. Unlike traditional relational database systems (RDBMS) that can’t measure to route large amounts of data, Hadoop assists businesses to run applications on thousands of nodes involving thousands of terabytes of data.
  • Cost effective: Hadoop allows businesses to simply access new data sources and rap into different types of data (both structured and unstructured) to generate value from that dataset. This means businesses can use Hadoop to develop valuable business visions from data sources such as social media, email conversations.  Hadoop can be used for a wide range of purposes, such as log processing, recommendation systems, data warehousing, and market promotion analysis and fraud detection.
  • Fast: Hadoop’s exclusive storage method is based on a distributed file system that basically ‘maps’ data anywhere it is located on a cluster. The tools for data processing are frequently on the same servers where the data is located, resulting in much faster data processing. If you are working with big sizes of unstructured data, Hadoop is able to capably process terabytes of data in just minutes, and petabytes in hours. To learn more about HDFS, click Big Data Hadoop Certification.
  • Resilient Feature: Fault Tolerance is the significant advantage of using Hadoop. During failure, when data is sent to a specific node, data is replicated to other nodes in the cluster.


Here are the disadvantages of Hadoop namely:

  • Security Concerns: Managing multifaceted applications such as Hadoop can be challenging. A simple example can be seen in the Hadoop security model, which is disabled by default due to absolute complexity. If whoever managing the platform lacks of know how to enable it, your data could be at huge risk. Hadoop is also missing encryption at the storage and network levels, which is a major selling point for government agencies and others that prefer to keep their data under wraps.
  • Vulnerable by Nature: Speaking of security, Hadoop makes running it a hazardous suggestion. The framework is written almost entirely in Java, which is one of the most widely used but yet, the controversial programming languages in existence.
  • Not Fit for Small Data: All big data platforms are not suited for small data needs whereas big data is not exclusively made for big businesses. Unfortunately, Hadoop is one of them. The Hadoop Distributed File System (HDFS) lacks the capacity to efficiently support the arbitrary evaluation of small files due to its high capacity design. As a result, it is not recommended for organizations with small quantities of data.
  • Potential Stability Issues: Like all open source software, Hadoop has had its share of problems on stability issues. To avoid these issues, organizations are intensely endorsed to make sure they are running the latest stable version, or run it under a third-party vendor equipped to handle such problems.

To know more about implementation of big data Hadoop, click on Hadoop Big Data Online Course.

For Big Data Hadoop Training needs, visit:

http://www.zarantech.com/course-list/hadoop. Call  515-978-9036 or email  ilyas@zarantech.com

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Companies Move On From Big Data Technology Hadoop

Companies-Move-On-From-Big-Data-Technology-HadoopIncreasing proof Hadoop — a standout among st the most critical innovations of the previous quite a long while for huge information examination — is not staying aware of the world that made it.

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Training and grooming professionals for Big Data industry

Alok KumarZaranTech LLC was featured in CIO Review Magazine July 2015 for their Big Data Analytics training. “To meet industry demand for Big Data Analytics while maintaining their expected high-quality service, ZaranTech selected blending training model as their training solution. Online learning for an organization is the vastly growing field” says Alok Kumar, Training Director at ZaranTech LLC. Online learning is not only about the inclusion of relevant information about the subject but it also includes the use of original and creative ideas .This makes the topic interesting and informative for the client. ZaranTech believes in delivering training that can be easily comprehended by the trainees with the attention to the minute details in the content.

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Big Trends in Big Data Analytics

HadoopHadoop has a set of tools to perform action on information. Distributed analytic frameworks, like Map Reduce, are evolving into distributed resource managers that are step by step turning Hadoop into a general information package, says Hopkins. With these systems, he says, “you will perform many alternative information manipulations and analytics operations by plugging them into Hadoop because the distributed file storage system”.The future state of huge information is going to be a hybrid of on-premises and cloud.

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Understanding Hadoop Technology

HadoopEnormous information is a prevalent theme nowadays in the tech media, as well as among standard news outlets. Also, October’s official arrival of huge information programming system Hadoop 2.0 is producing much more media buzz.  “To comprehend Hadoop, you need to comprehend two major things about it”. They are: How Hadoop stores records, and how it forms information. It is also said: “Envision you had a document that was bigger than your PC’s ability. You couldn’t store that record, correct? Hadoop gives you a chance to store documents greater than what can be put away on one specific hub or server.

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How Big Companies take advantage of Hadoop

HadoopAt this point, you have likely known about Apache Hadoop – the name is derived from an adorable toy elephant however Hadoop is everything except a delicate toy. Hadoop is an open source extend that offers another approach to store and process huge information. While expansive Web 2.0 organizations, for example, Google and Facebook use Hadoop to store and deal with their immense information sets, Hadoop has additionally demonstrated significant value for some organizations.

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Attend a Live WEBINAR about Big Data Hadoo Training on 9-July-2015 @8:00 PM CST ‪#‎ZaranTech


Time : Tuesday July 9th , 2015 @ 8:00 pm CST

You are most welcome to join our Upcoming batch, details of the same is as follows:

Demo Date   : 9th July Tue @ 8:00 PM CST
Class Schedule 13th July Mon, Wed & Fri 8:00 pm CST 3 hrs each session
Attend a Live Demo Session
: Click here to Register


Contact : Ilyas @ 515-978-9788 , Email : ilyas@zarantech.com

Demo Video by Trainer Raji

Attend a Free Live WEBINAR about Hadoop Training on 9-Jul-15 @8:00 PM CST. Register Link  – http://goo.gl/BYufNJ


Hadoop standardization required for industry growth

HadoopWith the latest versions of Hadoop being released the older versions are being modified and the behavior is changing for the same. Developers need to check for the changes in the applications. Since Hadoop platform is developing we need standardization of the process. Vendors and developers try to fix the applications and test them in multiple versions of Hadoop after releasing the product. This has resulted in slow migration of custom built apps to a better version of Hadoop. This complexity has given rise to a platform of Swiss-cheese matrix among st the vendors with customers having the option to choose between one tool and any other tools. They have to resolve the bugs and limitations.

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