Big data is an application of data science in which data analysts process massive sets of data in order to get some applicable format out of it. This form goes through various statistical methods to draw out hidden information and patterns which help companies to make strategic decisions. It is an important field because we are currently in the middle of a data crisis. A lot of companies store all kinds of data and store them for future reference. However, not all companies know how to deal with it. Companies need some employees to help them make the best use of this data.

Hadoop is a set of tools that help data analysts to conduct professional data analysis. Since it is possible for one computer to process terabytes of data, analysts use Hadoop to distribute this data among several servers and networks. This shared processing power is able to process the given data in less time. The bits of information and results are then aggregated to get the final insights. There are other tools as well. MapR is used to remove unnecessary information from the data set. Hive is a data query source that provides useful data sets in any format according to the preference of the data scientist.

Hadoop developers are individuals who make use of Big Data and Hadoop tools to build useful models for the clients. In order to become a successful Hadoop developer, individuals need to keep certain things in mind. In this article, we will discuss these specific points.


There are many market players such as IBM, Hortonworks, Cloudera, and Microsoft who need more data scientists to work for them. This is why these companies provide free tutorials to interested candidates. Even though the free courses do not have anything more than introductory material, it is a great way to show potential applicants how much scope there is in this field. Candidates can learn about the various tools involved such as MapR, Hive, Pig, and Spark. They also get practical knowledge about statistical programming languages such as R and Scala.


Reading books is a great way to study up on the newest technologies being used in the industry. There are several books that are published by professionals in the field who are aware of the best practices and the relevant tools. To gain insights from the experts helps a lot to make your own models which can be used by different companies. Some of the notable books to follow and read are Tom White’s Hadoop Guide, GarettGrolemund’s R for Data Science, Martin Kleppmann’s Designing Data-Intensive Applications, and Nathan Marz’s book on principles of Big Data.

3. Big Data Hadoop Training Course

Just like free courses, there are many paid courses as well. However, paid courses follow a much more structured format. They do not shy away from addressing some advanced topics. These training routines are primarily designed to equip employees with proper skills that they can use in a data science job. They usually come with certification programs and can be used in a recruitment meeting and can be shown to the hiring committee. Hadoop courses are offered by companies like Simplilearn, Udemy, Cloudera, Hortonworks, and Big Data university. They can be arranged for personal viewing, or they can be instructor-led.


No other method is as useful in this industry as building your own projects. Once candidates have got the most basic and rudimentary knowledge, they can start solving their own problems. They can use data science tools such as Apache Hadoop and Spark to build models that have applications for companies and customers. These models must solve some practical and common problems. That is how candidates will get noticed and their applicability is shown. A product demanded by a few people or nobody will not be able to make sales or give any profits to the firm.


Some of the most common programming languages used in data science now are Python, R, and Scala. However, other languages like Java and Javascript are not shy from showing their own roles. In fact, most of the Hadoop tools have been written with Java which is why Javascript architects are in high demand in the industry.

It is important that candidates follow these steps if they want to solidify their role in data science. The requirements are extremely flexible and the only way candidates can be valuable assets to the companies is by updating their own skills. They must have a practical mindset and possess critical thinking abilities.

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