How To Become A Big Data Developer

Big Data has been the buzz for quite some time now. Big Data related jobs are topnotch in the market today and there is a fair share of reasons for that. Data is generated every hour, every minute, every second. Therefore, enterprises need professionals to control this huge amount of data and utilize it to their benefit.

However, with perks come responsibilities. Therefore, building a career in big data is not an easy task. Apart from being a data savvy professional, you have to be an adept developer and an expert engineer.

A Big Data Developer typically caters to the specific Big Data needs of an organization and works to solving the Big Data problems and requirements. As a specialist, he or she should be skilled enough to manage the complete lifecycle of a Hadoop solution, including platform selection, requirement analysis, design of technical architecture, application design, development, testing, and deployment.

Skills You Need to Become a Big Data Developer

Entering the field of Big Data requires some basic skillsets. Look through them before you dig into the field.

  1. Problem Solving Aptitude

Big data is emerging and there are new technologies evolving everyday. As you dwell in the domain of big data, a new technology will come your way with every passing day. Therefore, to become a successful Big Data Developer, you should be a natural problem solver and tinkering with different tools and techniques should be your forte.

  1. Data Visualization

Big data comes in various forms, e.g. unstructured, semistructured, which are tough to understand.Therefore, to draw insights from data you need to get your eyeballs onto it. Multivariate or logistic regression analysis may be useful for a small amount of data but the diversity and quantity of data generated for a business necessitates the use of data visualization tools like Tableau, d3.js, etc.

Data visualization tools help reveal hidden details that provide critical insights to drive business growth. Furthermore, as you progress in your career as a Big Data Developer, you grow up to become a Data Scientist or a Data Artist when being well-versed in one or more visualization tools is a practical requirement.

  1. Machine Learning

Computational processing of the growing volumes and varieties of available data via machine learning makes it cheaper and more powerful. The need to know machine learning is also essential to a Big Data Developer’s career, because it makes possible to rapidly and automatically produce models to analyze complex data and deliver faster and accurate results on a large scale. Building precise models provides organizations with a better chance of identifying profitable opportunities.

  1. Data Mining

Data mining is a critical skill to be possessed by a Big Data Developer. Unstructured data comprise a huge amount of Big Data and data mining enables you to maneuver such data and derive insights. Data mining lets you sift through all the unnecessary and repetitive information in your data and determine what is relevant and then make use of that information to assess and predict outcomes.

  1. Statistical Analysis

Statistics is what big data is all about. If you are good in quantitative reasoning and have a background in mathematics or statistics, you are already close to become a Big Data Developer. Learn statistical tools like R, SAS, Matlab, SPSS, or Stata to add up to your skills and there is nothing that can stop you to become a good Big Data Developer.

  1. SQL and NoSQL

Working with Big Data means working with databases. This mandates the knowledge of a database querying language. As a Big Data Developer, you should be aware of both SQL and NoSQL. Although, SQL is not used to solve all big data challenges today, the simplicity of the language makes it useful in many cases. Gradually, distributed, NoSQL databases like MongoDB and Cassandra are taking over Big Data jobs that were previously handled by SQL databases. Therefore, the ability to implement and use NoSQL databases is a must for a Big Data Developer.

  1. General Purpose Programming

As a Big Data Developer, you need to code to conduct numerical and statistical analysis with massive data sets. It is essential to invest money and time to learn programming in languages like Java, C++, Python, Scala, etc. You need not master all of the languages.  If you know one language well, you can easily grasp the rest.

  1. Apache Hadoop

Hadoop is an indispensable technology for Big Data. Many-a-times, Hadoop is mistaken to be synonymous to Big Data. It is essential to be a master in Hadoop to become a Big Data Developer. The knowledge and experience of core components of Hadoop and related technologies such as HDFS, MapReduce, Flume, Oozie, Hive, Pig, HBase, and YARN will render you high in demand.

  1. Apache Spark

Spark is also an important technology to consider for big data processing. It is an open source data processing framework developed around speed, ease of use, and sophisticated analytics. Of course, Spark is not a replacement of Hadoop rather it should be looked at as an alternative to Hadoop MapReduce. Spark runs on top of existing HDFS infrastructure to provide enhanced functionality and it also supports the deployment of Spark applications in an existing Hadoop v1 cluster (with SIMR or Spark-Inside-MapReduce) or Hadoop v2 YARN cluster or Apache Mesos.

  1. Understanding of Business

After all, the main motive to analyse and process big data is to use the information for business growth. Hence, domain expertise empowers Big Data Developers to identify opportunities and threats relevant to the business and design deploy the solutions accordingly besides communicating the issues effectively with different stakeholders.

In Conclusion

Becoming a Big Data Developer requires proficiency in all the aforementioned skills. IT professionals may have an advantage in learning new programming languages and technologies but people from a statistical or mathematical background also have the advantage of an analytical mind.

However, remember that the more effort you put into acquiring the skills, the better you will be rewarded with a higher pay package. So, invest in yourself and hone your skills with time.

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