The secret for acquiring a big data job in 2015 is out now. Leverage your Big data techniques and tools for competitive advantage before they become commoditized. They are eight skills that you need to know to out stand for a Big Data job.
Its entering its second year now. After enjoying a immense popularity in 2014, it is all set to achieve new milestones in 2015. While Big Data platform is powerful, Hadoop requires care and feed by proficients. People experienced in MapReduce, Flume, Pig, HIVE, HBase and Yarn are in High Demand.
If hadoop is a well know quantity in Big Data world, Spark is a black horse which has a capacity to support its elephantine cousin. The sudden rise of In-memory stack is being proffered as a faster and an easy alternative in Map-Reduce analytics. Apache Spark is best positioned as one of the component in big data pipeline. Spark requires technical expertise to program and run.
On the operational side of Big Data, NoSQL databases like couchbase and MongoDB are taking over databases like Oracle and IBM DB2. NoSQL databases are often the source of data crunched in hadoop now Mobile apps and web apps. In the world of Big Data each and every skill occupy opposite sides of a virtuous cycle.
Machine Learning and Data Mining
People have been mining data as long as they have been collecting it. But in today’s Big Data world data mining has reached whole new level. Machine learning is one of the hottest fields in big data last year. Big Data pros who can harness machine learning to build and train predictive analytic apps are in super high demand.
Statistical and Quantitative analysis
This is what big data is all about. You are already half a way here if you have a good background in quantitative reasoning and a degree in field of mathematics or statistics. In addition you’ll be an expert in tools like R, SAS, Stata, Matlab or SPSS. In Past many of the quants went down to work on wallstreet. After Big Data boom, Companies in all sorts of industries around the country are in need of geeks with quantitative backgrounds.
The age of Data centric language is 40 years. But the grandpa still has a life yet in today’s big data world. While NoSQL won’t be used in all big data challenges, the simplicity of SQL makes it easy for many of them. SQL is seeing new life as the lingua franca for next generation.
Big data may be tough to understand. But in some cases, there is no replacement for actually getting your eyeballs onto the data. You can do different types of analysis until there is a problem. Sometimes, exploring a sample of your data in a tool like Qlikview or Tableau can tell you the shape of exact data. It also shows hidden data that change how you proceed.
General Purpose Programming Languages
Having experience in programming applications like C, Java, Python or scala cloud gives you edge over other candidates whose skills are confined to analytics. People who are comfortable with intersecting traditional app development and emerging analytics will be able to develop their own tickets and move freely between big data start ups and end user companies.