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Hire Top Hadoop developers with Ultragenius

Ultragenius is a marketplace for top software developers, engineers, programmers, coders, architects, and consultants. Top start-ups and companies choose developers from Ultragenius platform for their critical software projects. Hire from Ultragenius network & scale your engineering team in a matter of few days. 

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Rishabh is a software engineer having 7+ years of experience developing web applications that manage and track data at a very high speed.

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Mahesh is a software engineer having 3+ years of experience in developing Hadoop applications and turning functional requirements into extensive designs and architecture.

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John is a software engineer having 10+ years of experience in documenting, designing, and building robust Hadoop applications and testing various software prototypes.

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Hire top talented Hadoop developers with Ultragenius

Hadoop is widely used software for storing and processing large amount of data efficiently. Hadoop implements the mechanism of parallel processing by dividing the big task into several small tasks ensuring each tasks is operated by multiple slaves in the cluster.  

What Ultragenius offers?

Fast Hiring

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ultraGenius conducts tests and ensures that only the most suitable developer with the best skills is hired.

Hire Hadoop developers through Ultragenius in 4 easy steps

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UltraGenius is the best remote talent network to meet with brilliant developers who are most talented and skilled in the technical field. UltraGenius provides the most fit developers who not only match our job requirements but also our company's culture and working environment.
Once you are part of UltraGenius, you don't have to look for another platform which can find the top skilled developers with same effort as UltraGenius. UltraGenius's vetting tests and assessments are unmatchable and provides the perfect match for the job role.
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Want to hire Hadoop developers on your own? Here are the skills you must look for while hiring a Hadoop developer

Hiring Hadoop developers might be an intricate task for you if you are a non-technical manager. Hadoop is a prominent open source framework for handling a massive amount of data and its computation. Hadoop provides clustering multiple computers to parallely analyze datasets instead of using a single computer to store and process a large amount of data. But,  hiring the top talented Hadoop developers among thousands of developers is a challenging task for anyone. So, Ultragenius is here to assist you while recruiting the top talented  Hadoop developers on your own. Ultragenius understands your job requirements and gets you only the top developers who have sufficient experience working with Hadoop.

The following skills you must look for while hiring a Hadoop developer – 

Solid understnading of HTML and CSS

HTML (Hypertext Markup Language) and CSS (Cascading Style Sheets) are the two must knowing technologies for a Hadoop developer. You must hire such a developer who have worked with these technologies in-depth. Knowledge of Flexbox and CSS Grid, in addition to Bootstrap, Semantic and Structural Styling, and Foundation is a must to a Hadoop developer. Along with this, the developer should be well-versed in Javascript libraries especially jQuery and CSS grid systems. 

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Familiarity with Javascript fundamentals especially with ES6

Javascript is most widely used language in developing dynamic web applications and helps a developer integrate back-end with the front-end easily. The Hadoop developer must be clear with the fundamental concepts of Javascript language. ECMAScript 6 (or ES6) is the current version of ECMAScript and is widely used by developers up to a large extent. The Hadoop developers must be familiar with these ES6 skills –  

  • Arrow functions
  • Blocked scope constructors let and const
  • Advanced object literals
  • Template literals
  • Multi-line strings
  • Modules
  • Module loaders
  • Binary and octal literals
  • Reflect API
  • Proxies
  • Classes
  • Destructuring assignment
Knowledge of Hadoop Distributed File System

Hadoop Distributed File System (HDFS) is a storage component that manages massive amounts of data on a commodity hardware. Here are some characteristics of HDFS :

  • HDFS recovers faster from hardware failures.
  • HDFS is most suitable for streaming access to large datasets. 
  • HDFS supplies a strong bandwidth handling thousands of nodes in a single cluster.
  • HDFS is compatible with various platforms and operating systems. 

Check the Hadoop developers knowledge about NameNodes, DataNodes, Hadoop Daemons, Hadoop Architecture, etc.

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In-depth knowledge of Hadoop architecture

The Hadoop developer must strongly know about Hadoop ecosystem, components, and interfaces. The Hadoop Architecture comprises of four components – 

  • MapReduce – MapReduce enables parallel distributed processing which makes Hadoop to perform fast. 
  • HDFS – Hadoop Distributed File System (HDFS) stores the data in a large block rather than storing the data in small blocks. HDFS is designed on the distributed software concept.
  • YARN – Yet Another Resource Negotiator (YARN) performs tasks of Job Scheduling and Resource Management. The job scheduler divides the large processes into smaller ones and ensures that each process gets assigned to multiple slaves in a Hadoop cluster thus enabling parallel processing.
  • Hadoop Common – Hadoop Common is the java library that is needed for the components  present in the Hadoop cluster and is used by MapReduce, YARN, and HDFS for running the cluster.
Experienced in working with code versioning tools like Git, SVN, TFS

Hadoop Developers must possess excellent knowledge of version control systems, like Git, SVN, TFS, and Mercurial. Mostly, developers use Git for their work. It is the version control system that helps the team in collaborating and organizing your code, maintain the frequent changes that occur in the code. Git helps in reviewing the old codes and compares them with the newly updated code, pulling the code from their repository, and managing the commit history.

Check if the Hadoop developer knows about add, push, pull, and commit commands, branching, and merging as it allows developers to work independently on the code.

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Excellent grasping on Hadoop Hive, Hbase, and Pig

Big data Hadoop developer must know about Hive, Pig, and HBase.  Pig Hadoop is used for handling semi-structured data. HBase is used for handling unstructured data. Hive is designed to work quickly for large amounts of data.

  • Hive provides SQL-like query engine that allows users to read, write, and manage data. Hadoop Hive functions on the server-side of any cluster.
  • HBase is non relational database management system that is used to store and process large amounts of data. Check if the Hadoop developer knows about HMaster, Region server, and Zoom keeper.
  • Pig Hadoop is a very advanced tool that operates on the client-side of any cluster. Pig is well known for powerful transformation and enhanced data processing.
Experience working with HiveQL

Hadoop Hive provides a SQL-like query language for querying and handling massive datasets known as Hive-QL. Hive provides the facility to write the custom MapReduce framework that can perform  complex data analysis. Hive DDL commands are quite similar to SQL DDL commands – CREATE, DELETE, ALTER, DESCRIBE, SHOW, and TRUNCATE.

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Knowledge of Front-end frameworks and libraries

Hadoop developer must know about front end technologies and frameworks particularly React.js or Angular.js. These front-end frameworks are greatly required in today’s market. React is popular for its faster development of Single Page Applications while Angular uses interpolation, dependency injection, and eliminates coding mistakes by using the strongly typed Typescript that resolves many challenges faced by the developers.

Extensive knowledge of Java and Object Oriented Programming Concepts

Hadoop framework is written in Java. Java is used for storing, handling, and processing massive amount of data. Check if the Hadoop developer knows the fundamental concepts of Java like Multi-Threading, Exception Handling, Collections, String Handling, OOPS concepts (Data Encapsulation, Abstraction, Inheritance, and Polymorphism), and Generics.

Knowledge of MapReduce programming model

Traditional data programming model works by having a centralized server to store and perform calculations on data. However, it is not suitable for processing large amount of data. Therefore, MapReduce was introduced to solve handling big datasets. MapReduce is a programming model which divides the large data set into multiple smaller data sets and assigns them to multiple servers. Map splits and maps the data with key-value pair while Reduce sorts the intermediate data and reduces it into smaller data units. 

Knowledge of backend programming with Node.js

The Hadoop developers must have experience working with Node.js run-time environment. Many Hadoop application require integration with Node.js. Moreover, the developers must be aware of the existing packages which help in Big Data development like mesos, dockerode, hipache, overcast, spm-agent-nodejs, and dyndns.

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Hands-on knowledge of Linux commands

Linux provides a variety of tools to operate and compute the data sets.  Data processing and computing is much faster than Windows. Ubuntu is widely used operating system by big data professionals. Check if the Hadoop developers knows these commands  – cd, cd dir, ls, ls al, appendToFile, cat, checksum, chown, chgrp, chmod, copyToLocal, copyFromLocal, count, df, du, get, find, expunge, help, mkdir, ls, lsr, moveFromLocal, mv, put, rm, rmdir, rmr, stat, and tail.

Firm understanding of data loading tools like Flume

Flume is a robust, reliable, flexible, and configurable tool used for collecting, aggregating, and transferring massive amount of unstructured data from various data sources to Hadoop Distributed File System (HDFS). Main components include Flume Events, Flume Agents, Source, Channel, and Sink and additional components are  Interceptors, Channel Selectors, and Sink Processors. 

Proficient writing reliable, manageable, high-performance code

You must hire the Hadoop developers based on the ability to write proficient, high-quality, reliable, and high-performance code for big data applications. 

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Connect with the top 1% Hadoop developers of the world at the lowest prices

Hadoop is widely used open source framework that uses distributed storage for storing large datasets ranging from gigabytes to petabytes and processing big data using the MapReduce programming model. Hadoop provides a great flexibility computing massive amount of data. But recruiting the best Hadoop developers is not an easy task when a large no. of Hadoop developers are competing to grab the job opportunities.

Top Interview Questions to ask while hiring the best Hadoop developer

  • HDFS - HDFS stands for Hadoop Distributed File System. HDFS is responsible for storing massive amounts of datasets of unstructured and structured data. HDFS is comprised of two core components- 
  • NameNode - NameNode manages the metadata. It keeps track of all the files in the file system and arranges the replicate blocks when the required data blocks managed by the DataNode are not available. NameNode is also called Master Node.
  • DataNode - DataNode stores the actual data in HDFS as instructed by the NameNode. The DataNode performs read and write requests from the client’s file system.
    • YARN (Yet Another Resource Negotiator) - YARN is the widely used distributed operating system for managing data. The core components of YARN architecture include - 
    • Resource Manager - Resource Manager allocates the resources among all the applications. It includes two major components - Scheduler and Application Manager. Scheduler schedules the assigned tasks based on the available resources. It is not responsible for doing other tasks like monitoring and tracking the tasks. Scheduler does not guarantee to restart a process if it fails. Application Manager coordinates the process execution in an application and is responsible for accepting or rejecting the application when it is processed to the client. Application Manager guarantees to restart a process if it fails.
    • Nodes Manager - Nodes Manager launches and handles the node containers. It performs log management, monitors usage of resources, and also kills a container based on the instructions from the Resource Manager. 
    • Application Master - Application Master monitors the whole lifecycle of an application from requesting the required containers from the Resource Manager to executing specific operations on the obtained containers. 
    • Containers - Containers are the collection of physical resources like CPU cores, RAM, and disk on a single node. Container Launch Context (CLC) is the record that stores the environment variables, security tokens, dependencies, etc. CLC is responsible for invoking the containers.
  • MapReduce - MapReduce uses the concept of distributed and parallel algorithms which makes Hadoop a very efficient technology to use. Map() sorts and filters the data, organizes it into a group, and generates a key-value pair of data. Reduce() aggregates the output generated by Map() and clusters it into smaller set of data.


Apache Hive provides the facility of reading, writing,  and managing large data using SQL. Hive translates the HQL program into one or more Spark, Tez, or Java MapReduce processes. Then, it organizes the data into tabular format  for HDFS and run the processes to generate the result. 

HBase is written in Java and works as a column-oriented database that handles massive and sparse datasets. HBase stores data in individual columns and indexes them by a unique row key. The data is distributed over multiple servers which allows querying results within milliseconds.  Phoenix layer works on the top of HBase which allows the Hadoop developer to query, delete, and insert the data in the database.

These are the popular HDFS commands - 

  • ls - ls command lists all the files.
  • mkdir - mkdir creates a new directory.
  • touchz - touchz creates an empty file.
  • copyFromLocal - copies files/folders from local file system to Hadoop HDFS store,
  • cat - cat command reads a file and print its content on the standard output.
  • copyToLocal - copies files/folders from Hadoop store to local database system.
  • du - du command is used to check the size of the file.
  • text - HDFS prints the source file in text format.
  • put - put command copies one or more than one sources from local file system to the destination file system.
  • help - help shows the help for the specific command or for all commands if none of the command is specified.
  • usage - usage command shows the help for a specific command.
  • rmdir - removes an individual directory.
  • expunge - makes the trash empty.
  • mv - mv command transfers a file from source to destination. It can be used to move multiple files also when the destination is a directory.
  • count - count command counts the number of files, directories, and the bytes under the paths matching to a specific file pattern.
  • cp - cp command allows one to copy a file from source to destination. It supports copying multiple files in the case when the destination is a directory.
  • Hadoop1 has less components and functionalities as compared to Hadoop2.
  • Hadoop1 doesn't support non MapReducer model while Hadoop2 supports other distributed computing models as well like HBase, Spark, Message Passing Interface, Giraph, etc.
  • Hadoop1 follows the concept of slots that can run only Map task or Reduce task only while Hadoop2 follows the containers concept in which it is possible to run generic tasks.
  • There is  no windows support for Hadoop1 while Hadoop2 is supported in Windows also.

Inner bag is a kind of relation inside another bag. while an outer bag is a bag of tuples.

For example, (5,{(5,3,2),(5,4,4)}), this complete relation is an outer bag while {(5,3,2),(5,4,4)} is the inner bag.


Flume is a distributed system designed by Apache to store and transfer the large quantities of data from various web servers  to a centralized HDFS store like HBase for analysis.

Flume Event - Flume contains an array of byte payload which needs to be transported from source to destination with extra headers.

Flume Agent - Flume Agent receives the information from the client and transfers it to the destination (a sink or an agent). The flume agent has three main components - 

Source, Channel, and Sink. 

Source - A source receives the data from the data generators and moves the data to one or more channels in the form of Fluent events.

Channel - A channel acts as an intermediate that receives the data from the source and buffers it till it is consumed by all the sinks.

Sinks - A sink aggregates the data from the channel and stores it in the form of HBase, HDFS, Spark. 

The table data is stored in Apache Hive at location /user/house/warehouse in HDFS file system. Hadoop freelancers have to create these directories on HDFS file system before they use it. Here, you can find each and every directory of all the databases and all the sub-directories with the table name you make use of.

Frequently Asked Questions

Ultragenius is one of the leading platforms for hiring remote talent and connecting developer and part-time developers with Silicon Valley businesses. We focus on finding the best talents who will perform extremely well and will be easily integrated into your teams. We filter out only the top 1% most skilled developers among the 20K+ developers who have applied on our platform. Candidates have to prove their self-reported experience by giving Ultragenius’ s skill tests.

Ultragenius first tests the developer’s skill set by conducting a two and half hour hiring test. Our hiring test judges a candidate on all aspects like aptitude, case study analysis, verbal and reasoning, coding questions based on data structures and algorithms, software engineering, system design, and more. Then, there is another round for the candidates who are selected from this round called “Higher-level Assessment Skill Test”, which is a video round that deeply analyzes developers’ major skills and asks questions about the projects they have worked upon.

Fill up the form which is on every hiring developers’ page and we will inform you once we select the top 1% Hadoop developers matching your job requirements.  After analyzing the candidates based on their resumes and two assessment tests, we provide you the feedback quickly. And if the developers selected by our team are fit for your job role, then we also provide the onboarding.

Ultragenius offers you only the most skilled developers who are top 1% among the 20K+ developers who have applied on our platform. After a rigorous selection and testing process, we sort out only the top candidates for you. You can check out Ultragenius’ s selection process for hiring Hadoop developers on

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