Top 7 Must Have Skills For Landing Big Data Jobs In 2015
Summary
The job market for Big Data analytics is booming, and specific skills can land you dream offers. Proficiency in Apache Hadoop, Apache Spark, NoSQL databases, data mining, machine learning, quantitative analysis, SQL, and data visualization are highly sought after. General-purpose languages like C, Python, Java, or Scala are also valuable. The demand for experts in these areas is driven by Big Data's growing importance across industries. Demonstrating determination and adaptability to new technologies further enhances job prospects.
Table of Content
With a mad rush being the order of the day for gaining competitive advantage, jobs connected with Big Data analytics are hogging the limelight, and how. There is a scramble among aspirants, organizations and recruiters to leverage the many advantages of Big Data before the same is commoditized. If you are also in the Big Data job market in 2015, then these skills will garner you the job offer of your dreams. Read on for more.
1. Apache Hadoop
Hadoop, after having enjoyed immense popularity last year is all set for achieving newer milestones in 2015. With test clusters moving into production areas and software vendors increasingly targeting the processing and distributed storage architecture like never before, job markets are surely lapping up those with expertise in Hadoop. So, if you have knowledge of Hadoop’s core components such as MapReduce, Flume, Oozie, HDFS, Hive, Pig, YARN, and HBase, you can be confident of being in high demand.
2. Apache Spark
Apache Spark possesses the raw potential of eclipsing its mammoth cousin Hadoop and is expected to work as a faster and simpler option for MapReduce-style analytics. This in-memory stack works well with Hadoop frameworks and is being radically positioned as an important component in the big data pipeline. As it requires technical expertise for its programming and operations, Spark is offering job opportunities for aspirants in the know (also consider checking out this career guide for data science jobs).
3. NoSQL
When we take a closer look at the operational sides of Big Data, we observe that monolithic SQL databases such as Oracle and IBM DB2 are giving way to scale-out, distributed NoSQL databases of the kinds of MongoDB and Couchbase. For Big Data applications working on the mobile and web alike, NoSQL databases are also serving as hot sources for data crunching in Hadoop. On other words, as far as big data is concerned, NoSQL and Hadoop are occupying the opposite edges of a virtuous cycle. If you are proficient in these technologies, then you may look forward to getting into an organization dealing with Big Data, and very soon.
4. Data Mining and Machine Learning
The world of data mining and machine learning has acquired an altogether new definition with Big Data playing an important role in different industry verticals. With this in view, Big Data pros geared with the skills required for harnessing machine learning technology, building and training predictive analytic apps, or dealing with the processes of recommendation, classification, and personalization systems are bound to be in high demand. Are you ready to command your top dollar?
5. Quantitative and Statistical Analysis
Big Data is all about quantitative reasoning, mathematics, and statistics; so, if are adept in three fields then you are already halfway through. If you have advanced expertise in statistical tools like SPSS, Stata, SAS or Matlab, you can expect a better pay package for yourself. Yes, the big data boom has created a niche place for techies with quantitative backgrounds across the country and beyond.
6. SQL
Though it has 40 years of operations to its credit, this data-centric language is still applicable in the current big data age. SQL may not be competent enough to meet all big data challenges, but because of initiatives like those of Cloudera‘s Impala, this relational database is fast becoming a must for next-gen Hadoop-scale data warehouses.
7. Data Visualization
Big data makes it possible for users to indulge in logistic or multivariate regression analysis for telling the shape of data, and even revealing certain hidden details for letting you know how to proceed further. If you have knowledge of visualization tools, then you will surely emerge victorious with a job offer in hand (Here's the perfect parcel of information to learn data science).
Last but certainly not least; knowledge of general-purpose languages such as C, Python, Java, or Scala should also place you ahead in the crowd of job applicants; especially those who are confined to data analytics. Alongside, if you have a bulldog-like determination to find solutions and a natural desire to know more, then you will surely pick up the new technologies that will inevitably replace the ones listed here.
Go for these skills, a job offer is waiting to be grabbed somewhere!
