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9 Essential Skills For Your Big Data Career In 2015 |Big Data

9 Essential Skills For Your Big Data Career In 2015 |Big Data


In the rapidly evolving landscape of Big Data, mastering key skills is crucial for career success. Proficiency in Apache Hadoop, Apache Spark, NoSQL databases, data mining, machine learning, quantitative analysis, SQL, data visualization, and general-purpose programming languages can lead to lucrative opportunities. Problem-solving and creativity are also highly valued, as the industry seeks individuals who can adapt to emerging technologies and consistently provide innovative solutions in the dynamic field of Big Data.

It’s no longer a secret. Data mongers are scurrying to leverage the many tools and techniques of Big Data for gaining competitive advantage, much before their benefits start getting commoditized. So, if you are desirous of a lucrative job in the fast-increasing Big Data market, then it’s time to harness these essential skills, and on the double.

Apache Hadoop

It’s true that Apache Hadoop is on the verge of entering its 2nd decade soon, but then, it enjoyed a monstrous 2014 and is all geared to position itself with bigger wins in 2015. Today, more clusters are being moved into the production field. Software vendors are increasingly targeting those with prior experience and knowledge in the areas of processing architecture and distributed storage. But, even though the platform of Big Data is powerful, it requires the attention of proficient technicians. So, if you are adept at the core components of the Hadoop stack–such as Flume, HBase, YARN, Oozie, Hive, Pig, HDFS, and MapReduce – you can expect a very high demand for yourself.

Apache Spark

Spark, unlike Hadoop, which is a known quantity in the world of Big Data, can be referred to as a black horse candidate with raw potential of eclipsing its elephantine cousin. This in-memory stack is on a rapid rise and is gaining reputation as a faster and simpler option to analytics similar to MapReduce, either outside a Hadoop framework or within it. However, Spark is still in a position where it needs help to run and program itself, making it a green pasture for job applicants with the know (also consider checking out this perfect parcel of information for data science degree).


When we consider the operational sides of Big Data applications, scale-out NoSQL databases, such as Couchbase and MongoDB, are looking towards jobs that were earlier handled by experts in monolithic SQL databases such as IBM DB2 and Oracle. NoSQL databases, both in case of mobile apps and on the internet, act as valuable sources of data accumulated in Hadoop; along with acting as a hot destination for application changes requiring insights from Hadoop.

Data Mining and Machine Learning

In the competitive scenario that we are in today, Big Data mining has reached unprecedented levels, and how! Big data pros, with the ability of harnessing machine learning technology for building and training predictive analytic applications like recommendation, classification, and personalization systems, are all set to command top dollar and are in high demand in the job market (also consider checking out this career guide for data science jobs).

Quantitative and Statistical Analysis

If you boast of a degree in statistics/mathematics and have the powers of quantitative reasoning up your sleeve, you are almost there. The addition of expertise with statistical tools such as Matlab, SPSS, R, SAS, or Stata put you in better position with recruiters. Yes, job aspirants with a quantitative background can expect good opportunities in the existing markets.


Over 40 years in operation, this data-centric language is still alive and kicking. While it may be non-compatible with all challenges thrown in by Big Data, the simplicity of this Structured Query Language has to be learned to be understood to the core. With initiatives such as Cloudera‘s Impala rubbing shoulders with SQL, it may become the lingua franca for next-generation Hadoop-scaled data warehouses.

Data Visualization

Big data analytics can be tough, difficult to comprehend, and complicated for many. They require expertise in logistic or multivariate data regression analysis, along with proper expertise in the application of tools such as Qlikview or Tableau that reveals the shape of involved data and ways of proceeding with the same. If you are well-versed in visualization tools that draw merit, you can expect a fruitful career as a data artist.

Programming Languages for General Purposes

Prior experience in general-purpose programming languages like C, Python, Java, or Scala provides the edge over job candidates with skill sets confined to analytics. As per reports from Wanted Analytics, in 2014, there was a 337 percent escalation in the job postings for “computer programmers” having knowledge in data analytics. So, if you at the intersection of emerging analytics and traditional app development, you may move between big data start-ups and end-user companies alike.

Problem Solving and Creativity

Regardless of the number of advanced analytic techniques, tools and techniques that you boast of, the ability to think ahead and find your way out of a situation tops it all. With the implementation of big data consistently requiring new technologies and more evolved skills, your natural desire to find solutions, and your persistent bulldog-like determination, will always find many job offers waiting in the pipeline for your nod.


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