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Hadoop Developer Salary in India in 2024 [For Freshers & Experienced]

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21st Nov, 2022
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Hadoop Developer Salary in India in 2024 [For Freshers & Experienced]

 Doug Cutting and Mike Cafarella created Hadoop way back in 2002. Hadoop originated from the Apache Nutch (an open-source web search engine) project, which was further a part of the Apache Lucene project. The goal was to design an open-source framework that allowed for data storing and processing in a distributed and automated computing environment.

Hadoop is a software framework explicitly created for Big Data management, storage, and processing. It not only stores massive volumes of data, but it can also run applications on multiple clusters of commodity hardware. 

Hadoop boasts of a highly scalable architecture, such that it can expand from a single server to hundreds and thousands of machines wherein each machine provides computation and storage. Its distributed feature enables speedy and seamless data transfer among the nodes in the cluster, thereby facilitating continued functioning even if a node fails. 

Thanks to Hadoop’s distributed architecture, high scalability, high fault tolerance, enormous processing power, and fast processing speed, it is the perfect data management tool for businesses of all sizes. As a result, not only large corporations but also small and medium-sized businesses are adopting Hadoop. This growing adoption and demand for Hadoop services are creating a huge need for skilled Hadoop experts in the industry. Hadoop Developer is one of the many coveted Hadoop roles in demand right now.

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Who is a Hadoop Developer?

A Hadoop Developer specializes in handling and managing the requirements and processes associated with the Big Data domain. The job role is pretty similar to that of a Software Developer, with the only difference being that a Hadoop Developer focuses on Big Data.

Hence, Hadoop Developers must possess in-depth knowledge of Hadoop tools and concepts, be familiar with all the elements of the Hadoop ecosystem (HDFS, YARN, and MapReduce), and understand the individual functioning of those elements as well as how they work together within the Hadoop ecosystem. Hadoop Developers are primarily responsible for designing, developing, implementing, and managing Big Data applications.

The job of Hadoop Developers primarily revolves around Big Data. They collect data from disparate sources, clean and transform it, decode it to extract meaningful patterns, analyze it, and store it in a database for future use. They also prepare detailed visualization reports for the cleaned and transformed data using various Business Intelligence (BI) tools to help other stakeholders (particularly non-technical members) in the project understand the connotations of the extracted data.

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Responsibilities of a Hadoop Developer

  • To install, configure, and maintain the enterprise Hadoop environment.
  • To source and collect data from multiple platforms in large volumes.
  • To load data from different datasets and determine which is the best file format for a specific task. 
  • To clean data to best fit the business requirements at hand using streaming APIs or user-defined functions.
  • To build distributed, reliable, and scalable data pipelines for data ingestion and processing in real-time.
  • To create and implement column family schemas of Hive and HBase within HDFS.
  • To use different HDFS formats like Parquet, Avro, etc. to speed up system analytics.
  • To understand the requirements of input to output transformations.
  • To fine-tune Hadoop applications for improving their performance.
  • To define Hadoop job flows.
  • To review and manage Hadoop log files.
  • To create Hive tables and assign schemas.
  • To manage and deploy HBase clusters.
  • To build new Hadoop clusters as and when needed.
  • To troubleshoot and debug run time issues in the Hadoop ecosystem.

In-Demand Software Development Skills

Skills required to become a Hadoop Developer

Every Hadoop Developer must have the following skills:

  • In-depth knowledge of the Hadoop ecosystem, its various components, along with different tools including HBase, Pig, Hive, Sqoop, Flume, Oozie, etc.
  • In-depth knowledge of distributed systems.
  • The ability to write precise, scalable, and high-performance code.
  • Basic knowledge of scripting languages like Java, Python, and Perl.
  • Basic knowledge of database structures and SQL.
  • Excellent grasp over concurrency and multi-threading concepts.
  • Experience in writing Pig Latin scripts and MapReduce jobs.
  • Experience in data modeling with OLAP and OLTP.
  • Experience in working with various data visualization tools like Qlikview and Tableau.
  • Experience in working with ETL tools like Pentaho, Talend, Informatica, etc.
  • Strong verbal and written communication skills.
  • Analytical and problem-solving skills.
  • Business acumen and domain knowledge.

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Also read: Data Scientist Salary in India

How to become a Hadoop Developer?

To become a Hadoop Developer, it is not mandatory to come from a Computer Science background – any related specialization such as Statistics/Mathematics/Data Analytics/Information Science will bode well for the job profile. After obtaining your graduate/postgraduate degree, the first step to becoming a Hadoop Developer would be to focus on acquiring the right skills for the job profile. So, keeping in mind the skills we’ve listed above, you must: 

  • LearnJava, and SQL.
  • Get familiar with Linux.
  • Work with MapReduce algorithms.
  • Learn different database concepts.
  • Learn the nitty-gritty of Hadoop ecosystem
  • Learn different Hadoop and HDFS commands.
  • Start writing beginner-level code for Hadoop.
  • Dig deeper into Hadoop programming. 
  • Take up production-grade Hadoop projects.

Apart from these steps, here are some tips that will help you become a good Hadoop Developer:

  • Own the data – Since the job requires you to spend a great deal of time in collecting, cleaning, and transforming the data for further analysis and storage, you must dig deep into the data you are working with. This will help you to gain the optimum beneficial insights from the data. 
  • Be ready to learn new things – You should always be open to learning new concepts and new technologies that could help you improve your Hadoop projects and applications.
  • Focus on learning Data Science techniques – Invest your time to learn about the different Data Science techniques such as data mining, data transformation, data visualization, among other things. This will help you to use the data to its maximum potential to solve diverse business challenges.

Hadoop Developer Salary in India

Hadoop Developers can find job opportunities across various sectors of the industry, including IT, finance, healthcare, retail, manufacturing, advertising, telecommunications, media & entertainment, travel, hospitality, transportation, and even in government agencies.

However, the six major industries that are driving the demand for Hadoop talent in India are IT, e-commerce, retail, manufacturing, insurance, and finance. Of all the industries, e-commerce records as having the highest Hadoop salaries in India. From big names like Amazon, Netflix, Google, and Microsoft to startups like Fractal Analytics, Sigmoid Analytics, and Crayon Data – every company is investing in Big Data and Hadoop talent. 

The Hadoop Developer salary in India mainly depends upon a candidate’s educational qualifications, skill set, work experience, and the company size and reputation, and job location. For instance, candidates who have a postgraduate degree can earn a starting package of around Rs. 4 – 8 LPA.

However, graduate freshers can earn between Rs. 2.5 – 3.8 LPA. Similarly, professionals who possess the best combination of the skills we’ve mentioned above can earn anywhere between Rs. 5 – 10 LPA. Mid-level professionals in a non-managerial capacity receive an average annual package of Rs. 7 – 15 LPA and those in managerial roles can make around Rs. 12 -18 LPA or more.

The salary scale of senior-level Hadoop Developers (with over 15 years of experience) is usually very high, ranging between Rs. 28 – 50 LPA or more. 

The global Hadoop Big Data market is projected to grow from US$ 4.91 billion in 2015 to US$ 40.69 billion by 2021, recording a CAGR (Compound Annual Growth Rate) of 43.4% during the forecast period. This indicates positive growth in the demand for Hadoop Developers in the years to come. 

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Job roles for Hadoop Developers:

The knowledge of different job roles related to Hadoop developers can help you to determine which one to choose.

1.Hadoop Software Engineer

A Hadoop software engineer can work with a software development team that works on the company’s current projects. Some of the key duties of this job role include developing computer code validation and testing tactics and working on software programming. These engineers work closely with shoppers and other departments to convey project tenders and statuses.

 2. Hadoop Senior Software Engineer

They are proficient at working on the latest software technologies capable of solving business concerns. The term “senior” means that they possess big data skills using Storm/Hadoop and ML algorithms to solve business issues. Moreover, this category of Hadoop developer possesses an in-depth understanding of distributed systems and is an expert at using corresponding frameworks to make applications more powerful.

3. Hadoop Software Developer

They look after Hadoop applications’ programming. Some of their job duties resemble that of software system developers. They are also proficient at developing Hadoop applications and systems.

They must be acquainted with the big data fundamentals to perform their job duties flawlessly.  Furthermore, they know data manipulation, storage, amendments, and decoding.

4. Data Engineer

They optimize data and the data pipeline-based design. They are also proficient at data pipeline building and data wrangling for building data systems and optimizing them.

They can indirectly assist software system developers, data analysts, info architects, and data scientists. They assure outstanding data pipeline design when they work with these professionals.

This job role of a Hadoop developer demands that professionals must be independent and comfortable when fulfilling the needs of multiple systems and groups. Moreover, they are proficient at redesigning the business’ data design to facilitate cutting-edge data and products.

List of Companies hiring for the position of Hadoop jobs in India

  • Cognizant
  • Infosys
  • Amazon
  • Alteryx
  • Ayata
  • Flipkart
  • IBM
  • United Health Group
  • TCS

 

Benefits of learning Hadoop

1) Data safety:

Hadoop’s excellent fault tolerance ability makes it a suitable choice for large-scale companies looking to protect their data. It provides high-level protection for single and multiple data failures. Hadoop’s internal working implies that the data is conveyed to individual nodes wherein the data replicates to other nodes. You can expect a high Hadoop admin salary in India if you are proficient at ensuring the organization’s data safety.

2) Affordability:

The business’ datasets tend to increase with time. Hadoop offers an effective solution for the proper storage of voluminous data. The use of conventional RDBMS proves to be expensive for organizations to scale up their data. Thus, Hadoop offers an affordable solution for data scalability. When using those conventional systems, organizations occasionally have to restrict their data, but this issue is not found when using Hadoop.

It can store approx. hundreds of pounds for every Terabyte. So, it is useful as an authentic data storage solution for the voluminous data intended for future use. A decent big data Hadoop salary is guaranteed if the developers can proficiently explore all the benefits of Hadoop.

3) Scalability:

Implied from the name itself, it indicates the capability to manage massive data for growth purposes. Hadoop is one of the greatest scalable platforms when it comes to data storage. This is because it has the potential to disburse massive datasets among various parallel servers.

The conventional RDBMSs can’t scale huge volumes of data. Conversely, Hadoop can work on a myriad of nodes. The Hadoop admin salary in India is inclusive of how skilfully the developers can scale the data.

4) Quick operation:

The data present on the Hadoop system is allocated on a file system within a cluster called ‘Maps’. One of the unique features of Hadoop is the distributed file system. Hadoop facilitates the quick processing of data via the same servers that process the data. Moreover, it can process unstructured data at a speed of a few terabytes within a few minutes.

5) Versatility:

Hadoop supports structured as well as unstructured data. So, it facilitated the organizations to provide hassle-free access to different data sources. This is possible by simply switching among different data types. You can use Hadoop to deliver valued business insights from varied sources such as social media platforms, emails, clickstream data, etc.

Hadoop is also useful in log processing, data warehousing, market campaign investigation, fraud detection, and recommendation systems. So, the versatility of Hadoop suggests the outstanding Hadoop admin salary in India for skilled candidates.

6) Wide range of applications:

Hadoop provides topmost priority to data, and so it deters data loss. It makes the most of the data. Its architecture involves creating comprehensive sets of data rather than developing data samples for analysis. The comprehensive datasets lead to in-depth data analysis and provide optimal solutions. One of the reasons why many companies are happy to offer high big data Hadoop salary is that the developers can work on various types of applications.

7) Outstanding career opportunities:

Considering the huge share of organizations actively working with big data, Hadoop will have a considerable share in job opportunities in the future. The developers must have exceptional skills for data harnessing. So, Hadoop looks after framing cost-effective plans. In such cases, there will be more chances of obtaining a handsome big data Hadoop salary.

Conclusion

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We hope you liked our article on Hadoop developer salary in India. These numbers above are not set in stone. The real influencer of your salary is the skills you have,  the mastery you have attained over them, and how quickly you grow and make the company grow as well.

If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

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Frequently Asked Questions (FAQs)

1What is Hadoop?

Hadoop is an open-source tool for building data processing applications that run on a distributed computer system. Hadoop applications work on enormous datasets spread across large cluster computers. Disposable computers are inexpensive and widespread. These are mainly used to increase the computing power at a minimal cost. Data in Hadoop is stored on a distributed file system known as the Hadoop Distributed File System. The processing paradigm is built on the Data Locality idea in which computational logic is supplied to data cluster nodes. An application like this works with Hadoop Distributed File System data.

2What are the advantages of using Hadoop?

Hadoop provides several benefits. To begin with, it is quick since the data is scattered across the cluster and mapped in HDFS, allowing for speedier retrieval. Even the data processing tools are frequently hosted on the same computers, saving processing time. Second, it is scalable since a Hadoop cluster can be expanded by simply adding nodes. Hadoop is also open source and stores data on commodity hardware, making it far more cost-effective than traditional relational database management systems. Finally, Hadoop is robust to failure because HDFS has the property of data replication over the network, which means that even if a network failure occurs, Hadoop will utilize the other copy of data.

3What are the modules in Hadoop?

Hadoop has four modules, which are well-known and frequently used. The first is MapReduce, which is a computing paradigm and software framework for developing Hadoop applications. These MapReduce applications can handle massive amounts of data in parallel on big clusters of computers. The second is HDFS, which is responsible for Hadoop applications storage. It copies data blocks and disperses them across a cluster of computing machines. This distribution allows for accurate and fast calculations. YARN, another Hadoop module, is a Resource Negotiator that is used for task scheduling and cluster management. And the last one is Hadoop Common, which is a Java library used in Hadoop.

4What is Hadoop?

Hadoop is an open-source tool for building data processing applications that run on a distributed computer system. Hadoop applications work on enormous datasets spread across large cluster computers. Disposable computers are inexpensive and widespread. These are mainly used to increase the computing power at a minimal cost. Data in Hadoop is stored on a distributed file system known as the Hadoop Distributed File System. The processing paradigm is built on the Data Locality idea in which computational logic is supplied to data cluster nodes. An application like this works with Hadoop Distributed File System data.

5What are the advantages of using Hadoop?

Hadoop provides several benefits. To begin with, it is quick since the data is scattered across the cluster and mapped in HDFS, allowing for speedier retrieval. Even the data processing tools are frequently hosted on the same computers, saving processing time. Second, it is scalable since a Hadoop cluster can be expanded by simply adding nodes. Hadoop is also open source and stores data on commodity hardware, making it far more cost-effective than traditional relational database management systems. Finally, Hadoop is robust to failure because HDFS has the property of data replication over the network, which means that even if a network failure occurs, Hadoop will utilize the other copy of data.

6What are the modules in Hadoop?

Hadoop has four modules, which are well-known and frequently used. The first is MapReduce, which is a computing paradigm and software framework for developing Hadoop applications. These MapReduce applications can handle massive amounts of data in parallel on big clusters of computers. The second is HDFS, which is responsible for Hadoop applications storage. It copies data blocks and disperses them across a cluster of computing machines. This distribution allows for accurate and fast calculations. YARN, another Hadoop module, is a Resource Negotiator that is used for task scheduling and cluster management. And the last one is Hadoop Common, which is a Java library used in Hadoop.

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