In large commercial data before using big data analysis tools and techniques to gain competitive advantage is no longer a secret. By 2015, if you are looking for work-related big data is still in the workplace, then, nine kinds of skills presented here will help you get a job offer.
1. Apache Hadoop
Hadoop has now entered its second decade of development, but it is undeniable that, Hadoop development spurt occurred in 2014, due to the Hadoop cluster continues to shift from testing to production and software vendors direction, which is getting closer distributed storage and processor architectures, therefore, this momentum in 2015 will be more severe. Because of the powerful big data platform, Hadoop can be a picky monster, it needs to be familiar with the technical staff of attentive care and feeding. Hadoop master core technologies (eg, HDFS, MapReduce, Flume, Oozie, Hive, Pig, HBase, and YARN) demand for technical personnel in the workplace will increase.
2. Apache Spark
If Hadoop has been widely known in the world of big data, the Spark is a dark horse, it contains the potential to make Hadoop eclipsed the original. Whether or not in Hadoop architecture, the rapid rise of in-memory computing technology is considered MapReduce style analysis framework for faster and more compact alternative. Spark best positioning should be an important member of a large family of data technologies. Spark is still a need for programming expertise and operational, providing good jobs for engineers know the technology.
3. NoSQL
At the operational level big data, such as MongoDB and Couchbase a distributed, scalable NoSQL database are taking over market share in a very large SQL databases, such as Oracle and IBM DB2. In the WEB and mobile app level, NoSQL databases are often used as Hadoop analysis of data sources. In large data field, Hadoop and NoSQL two endpoints were to become a virtuous circle.
4. Machine Learning and Data Mining(机器学习和数据挖掘)
人们习惯于对收集的数据进行挖掘，但是, 在当今大数据的世界里, 数据挖掘已经达到了一个全新的高度。机器学习成为去年大数据技术最热门的领域之一, 2015年顺理成章地成为它的突破之年。大数据将会使那些能够利用机器学习技术去构建和训练像分类、推荐和个性化系统等预测分析应用程序的人成为职场宠 儿, 取得就业市 场上的顶级薪金。
5. Statistical and Quantitative Analysis(统计和定量分析)
这就是大数据。如果你有定量推理背景和数学或统计学等方面的学位,那么你就成功了一半。此外,再加上一些使用统计工具经验,例如 R, SAS, Matlab, SPSS, 或者是 Stata, 你就能够锁定这些工作岗位啦。在过去,许多量化工程师都会选择在华尔街工作, 但由于大数据的快速发展, 现在各行各样都需要大量的具有定量分析背景的 极客。
6. SQL
以数据为中心的语言已有超过40年的历史了, 但是这种祖父级的语言在当前的大数据时代仍然具有生命力。尽管它难以应对大数据的挑战 (见上文NoSQL部分), 但是, 简化了的结构化语言使其在许多方面变得十分容易。同时应该感谢来自于Cloudera所发布的Impala等开源项目, SQL获得了新生, 成为下一代Hadoop规模的数据仓库的通用语言。
7. Data Visualization(数据可视化)
大数据可能不是那么容易理解, 但在某些情况下, 通过鲜活的数据吸引眼球仍然是不可替代的方法。你可以一直采用多元或逻辑回归分析方法解析数据, 但是, 有时候使用类似 Tableau 或 Qlikview 这样的可视化工具探索数据样本能够直观的告诉你所拥有的数据的形态, 甚至是发现那些能够改变你处理数据方法的一些隐蔽细节。当然，如果你长大后想成为数据艺术家, 那么, 精通一个甚至是更多的可视化工具就是必不可少的了。
8. General Purpose Programming Languages
Have experience in programming applications similar to Java, C, Python, Scala, etc., or in common language enables you relative to those who limited analytical techniques have an advantage. The number of jobs according to Wanted Analytics statistics, recruitment data analysis has background "computer programming" grew by 337%. People with traditional application development and emerging data analysis capabilities will have a great choice of employment, to freely flow between the end-user companies and big data startups.
9. Creativity and Problem Solving (creativity and problem solving skills)
Whether you have much advantage in advanced analysis tools and techniques, independent thinking is still irreplaceable. Big data processing tools will inevitably evolve development, new technologies will continue to emerge and alternative technologies listed here. However, if you thirst for new knowledge and instinct, and to find solutions to the same problems as the hound, there will be plenty of job opportunities waiting for you.
