9 skills required by Big data engineers in 2016

Apache Hadoop

Hadoop is now in its second 10-year development, but it is undeniable that Hadoop has developed in the 2014, with Hadoop moving from test clusters to production and software vendors, which is increasingly close to distributed storage and processor architectures, so This momentum will be more intense in 2015 years. Because of the power of the big Data platform, Hadoop may be a picky monster that requires the attentive care and feeding of familiar technicians. Technicians mastering the core technologies of Hadoop (for example, HDFS, MapReduce, Flume, Oozie, Hive, Pig, HBase, and YARN) will increasingly need to work in the workplace.

Apache Spark

If Hadoop is widely known in the Big Data world, Spark is a dark horse, and its original potential is overshadowed by Hadoop. Whether or not it is a Hadoop architecture, fast-rising memory technology is considered a faster and cleaner alternative to the MapReduce style analysis framework. The best positioning for spark should be one of the important members of the big Data technology family. Spark still needs the expertise to program and run, which provides a good job opportunity for engineers who know the technology.

Nosql

At the operational level of big data, distributed, extensible NoSQL databases such as MongoDB and Couchbase are taking over the very large market share of SQL databases such as Oracle and IBM DB2. At the WEB and mobile app level, NoSQL databases are often used as data sources for Hadoop Analytics. In the big Data World, Hadoop and NoSQL are the two endpoints of a virtuous cycle, respectively.

Machine learning and data Mining (ML and DM)

People are used to digging up collected data, but in today's big data world, data mining has reached a whole new level. Machine learning became one of the hottest areas of big data technology last year, and 2015 was a logical breakthrough for the year. Big data will make it possible for those who can use machine learning technology to build and train predictive analytics applications like classification, recommendation, and personalization systems to become career darlings and get top salaries in the job market.

Statistical and quantitative analysis (statistical and quantitative)

This is big data. If you have a quantitative reasoning background and a degree in math or statistics, then you're half done. Plus, with some experience using statistical tools such as R, SAS, Matlab, SPSS, or Stata, you'll be able to lock in these jobs. In the past, many quantitative engineers have chosen to work on Wall Street, but with the rapid development of big data, it is now necessary to have a large number of geeks with a quantitative background.

Sql

The data-centric language has been around for more than 40 years, but this grandparent's language is still alive in the current era of big data. Although it is difficult to cope with big data challenges (see the NoSQL section above), the simplified structured language makes it easy in many ways.

Data Visualization (visualization)

Big data may not be easy to understand, but in some cases attracting eyeballs through fresh data is still an irreplaceable method. You can always use multivariate or logistic regression analysis to parse data, but sometimes using a visualizer like Tableau or Qlikview to explore data samples can tell you the shape of the data you have and even uncover some of the hidden details that can change the way you handle your data. Of course, if you want to be a data artist when you grow up, mastering one or even more visualization tools is essential.

General Purpose Programming Languages

Having programming experience in a common language like Java, C, Python, or Scala can make you more competitive with people who are limited to analytical technology. According to wanted analytics, the number of "computer programming" jobs with data analysis backgrounds has increased by 337%. People with traditional application development and emerging data analysis capabilities will have a huge choice of jobs and are free to flow between end-user businesses and big data startups.

Creativity and problem solving (creativity and problem-solving skills)

Regardless of your strengths in advanced analytical tools and technologies, the ability to think autonomously remains irreplaceable. Big data processing tools will inevitably evolve, and new technologies will continue to emerge and replace the technologies listed here. But if you can instinctively crave new knowledge and find solutions to problems like hounds, there will be plenty of job opportunities waiting for you.

9 skills required by Big data engineers in 2016