Hadoop was born from the need of companies to store and process big data. Hadoop is a framework that allows users to keep all the big data in an environment which is distributed. This feature to be able to store data in a distributed environment allows the data to be processed parallely. Parallel processing of data not only allows for a faster alternative to handle all the behemoth piles of data, but it also allows for flexibility, which had always been lacking from traditional big data handling methods.
We live in a world where the data is practically everywhere. We live and breathe data. All the data that we generate might seem meaningless to us, but to organizations like Google, Amazon, and Facebook, this data is precious. Leaving aside the big tech giants now, organizations irrespective of their size and sector are realizing the potential of big data. For these organizations, big data helps them gain business insights, the like they have never seen before, which has helped them in their decision-making process.
All these organizations that are slowly adopting the way of big data also need a platform or rather a tool to read and analyze the data. To smoothly bridge this need of a device, Hadoop comes into the game. So, it is needless to say that if someone is thinking of making a career out of big data, making a career is essential. Now that you have seen the importance of Hadoop firsthand, let us now discuss what career opportunities in Hadoop are there, but before that, let us see all the skills needed to have a successful career in Hadoop.
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Making a Career in Hadoop
1. Required Skills
There are no such “skills,” or there is no need to have any specific background to make a career in Hadoop or big data. With that being said, there is, however, knowledge of certain things that should help to ease your way into becoming a master in Hadoop. So, if you happen to have any working experience with any Linux based operating system, you would get a head start in learning Hadoop.
Similarly, any prior knowledge of programming languages like Scala, Python, or Java would help you in writing your first MapReduce program in various languages and assist you in doing parallel processing over the Hadoop storage (HDFS) framework. Any knowledge of SQL will have you learning the ecosystems in Hadoop like Hive, Pig, etc. in no time. If you happened to know databases like NoSQL, you would definitely feel at home in writing and working with the HBase database.
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2. Professionals enjoying their transition to Hadoop
Making a big name in Hadoop or big data is not industry dependent; however, certain professions make this leap easier than the rest. So, if you happen to be a developer, BI/DW/ETL professional, some senior IT professional, Fresher, Mainframe professionals, etc. making a jump into Hadoop should be a very easy task. Additionally, anyone having an IT background should not face any particular problems in making a career in Hadoop.
3. Expected Salary
Now, this is a very tricky land to tread on, while Hadoop professionals are highly sought after; however, the money that they make is highly dependent on the place that they live. So, big corporations in the United Kingdom like, Explore group, BBC, Eames Consulting Group, provide an average of 50 Great British Pounds to Hadoop developers. Thus, the average salary that any Hadoop professional would receive in the United Kingdom is about 66, 250- 66, 750 Great British pounds.
Meanwhile, in the United States, the average money which the Hadoop professionals make ranges from 95k-102k United States Dollars (according to indeed.com). Here in India, on average, any Hadoop Developer’s salary would be in the range of 4-6 lakhs Indian Rupees. The average money which a Java and Hadoop developer makes in the famous company Tata Consultancy Services is around 677-735 thousand Indian Rupees.
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Career Opportunities in Hadoop
1. Sector wise requirement of Hadoop professionals
a. The finance and banking sector
The use of big data and hence the Hadoop framework in the sector of finance banking allows for frauds and security breaches very early. Big data is a staple in detecting frauds, following audit trails, and reporting the enterprise credit card risk. The information that is collected from the customer is transformed to be analyzed with precision to provide better insights and improve the capability to come to a decision. If you couple NLP or natural language processing with the use of big data, this would allow professionals to catch illegal trading very quickly.
b. Media, Communication and Entertainment Sector
The data collected under this domain is stored, processed, and used to create the recommender engines that you see from websites like Amazon and Netflix. This sector also uses the data available on various social media platforms as well. This social data could then be used to perform sentiment analysis on something like a Wimbledon game or Messi leaving Barcelona.
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c. Healthcare
We all have been the victim of inflation, even in the sector of health care. But, due to the use of big data technologies in the healthcare sector, this cost can actually be reduced quite significantly. One can take the data like patient history and disease history to treat the illness that is plaguing the patient accurately. Moreover, the use of a Convolutional Neural Network can actually help in detecting diseases like cancer or tumours in a very early stage.
d. Education sector
The career opportunities in Hadoop in the education sector are limitless. We are only just able to think of the vast number of possibilities that big data would have on students and education. Like, the University of Tasmania is collecting the data of over 26000 students. The data that has been collected is the amount of time that the student spends on specific pages and the overall progress that the students are making. The information that is collected with this process would then be used to transform the education system to help every student achieve their potential.
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e. Transportation Sector
Self-driving, which has been hailed as the future of transportation, is nothing but the car running on Big data to steer its course. The data which is fed from all the various sensors that the vehicle is fitted with goes through a mathematical model to provide the result that we need. Not just this, but the location data which these social networking sites collect and the data which comes from the high-speed telecom have been used to transform the entire transportation sector. The analytical side of big data is being employed to control vehicles’ behaviour, plan out the route, effectively control the traffic, help reduce congestion on roads, manage the revenue, etc.
f. Energy and Utilities Sector
It is estimated that about 60% of the existing electricity grid would need a change sometime this decade. People have just started to adopt smart meters as main-stream. These smart meters allow the user to have more control and gain better insights into how they are using electricity. The data that these intelligent meters collect also helps the corporations effectively plan out the electricity requirements of a particular place and ensure that they get electricity as per their demand.
2. Some job titles for Hadoop professionals
a. Hadoop architect
It is needless to say that Hadoop is becoming the new data warehouse. It has become the source of data in the various companies, replacing the traditional methods. The ones who are well-versed with this framework’s working get paid handsomely for the help they provide to the organization that employs them. So, a Hadoop architect is supposed to dictate the path the organization should take to deploy big data Hadoop-related technologies.
They are also supposed to come up with a blueprint or a roadmap in deciding how the company should move ahead. Ant good Hadoop architect is supposed to know and should have hands-on experience with platforms like Cloudera, MapReduction, Hortonworks, among the few. They are ones who take responsibility for the life cycle of Hadoop in the company.
A Hadoop architect is supposed to bridge the gap between Big data engineers, data scientists, etc. to the organization’s needs. They should also have in-depth knowledge of all the Hadoop architecture like HDFS, Pig, Hive, etc. They are also responsible for choosing the solution, which should provide the least amount of hindrance in the deployment phase.
b. Hadoop Administrator
This is one of the central roles pertaining to Hadoop in any organization. A Hadoop administrator, while having the roles and responsibilities similar to that of a System Administrator, is also supposed to ensure that there is no roadblock, and Hadoop should function smoothly in the organization. They are supposed to maintain the clusters of Hadoop, check and monitor the working of the entire system routinely.
They should be able to plan to either upsize or downsize whenever the need for doing either arises. They should also monitor the functioning of HDFS and should ensure that it works all the time correctly. They are also the ones that decide the amount of clearance any person has to the data. Any good Hadoop Admin should be adept in technologies like HBase, Linux scripting, HCatalog, and Oozie.
c. Hadoop Tester
Since the size of Hadoop networks is increasing day by day, the importance of having a Hadoop tester in an organization is also growing. As the name suggests, a Hadoop tester is supposed to test out the Hadoop framework, which has been installed in the company. They also are responsible for checking for aspects like viability, security flaws, etc. They are also tasked to report and rectify these issues that they come across.
The primary role of a Hadoop tester is to troubleshoot the problems. The earlier they find the underlying issues, the better. So, a Hadoop tester should have knowledge of all the frameworks that the company has currently deployed along with all the scripts that are running to augment the Hadoop framework. They should also know how to work with selenium in creating an automatic testing system for the Hadoop framework in the company.
Conclusion
The world of big data is growing exponentially during recent times. The growth of computing power has a lot to do with making the various big data-related fields open and accessible to almost everyone regardless of the discipline. Since we are increasing our data footprint by terabytes each day and considering the sheer value of the data, frameworks like Hadoop must be making their way into many developers’ lives.
If you are considering a job in this field of big data career in Hadoop is one of the safest bets. You would be central to any big data-related task in your company, and there are many career opportunities in Hadoop for you to choose from. However, if you choose a career in Hadoop, make sure that you augment your knowledge with frameworks like a spark to improve your employability further.
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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What are some of the most common Data Platforms?
The outreach of the Internet to the far corners of the world has led to the generation of a huge amount of data. Hence, the Big Data market is increasing at an accelerating pace too. In fact, the Big Data market is expected to grow to USD 274.3 billion in 2022. The massive amount of data generated would also need to be stored and converted onto the relevant form to gain insights. A Data Platform is a technological solution for various data-related needs such as processing, analysing, and data governance. Many Big Data platforms serve this very purpose, such as Google Cloud, Cloudera, MongoDB, Cassandra, and Hadoop.
What are the different components of YARN?
YARN (Yet Another Resource Negotiator) was first introduced in Hadoop 2.0 as an improvement over the Job Tracker present in Hadoop 1.0. Its main objective is to allocate system resources to the applications running in the Hadoop cluster. YARN has 4 essential components: Resource Manager, Node Manager, Containers, and Application Master. The Resource Manager manages the resources used across the Hadoop Cluster. Node Manager executes the task in each data node. Containers are basically the space where the YARN application is run, and the Application Manager monitors the task execution by working alongside the Node Manager.
What is the difference between HDFS and NAS?
HDFS (Hadoop Distributed File System) is a Java-based, primary data storage unit of Hadoop. Its main purpose is to store large quantities of data running on a cluster of community hardware. On the other hand, NAS (Network Attached Storage) files storage stores data on dedicated hardware. It provides access to data to heterogeneous client groups and multiple users. HDFS is comparatively cost-effective as it includes commodity hardware, whereas NAS contains high-end storage devices, making it costlier.
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