Is Java important for big data

You have to be able to do that for big data

The topic of big data is complex and multi-layered - the range of knowledge of a data scientist looks just as diverse. Know-how from several specialist areas is therefore important for working in big data projects.

Programming skills
For all those who come from traditional IT, this area is the most obvious. When it comes to programming languages, Python, Java, R and C ++ are recommended. The most important framework for Big Data - Apache Hadoop (see below) - is based on Java, so the programming language is essential to be able to work with Hadoop.

Data structures and algorithms
What options are there to save and organize the data to be analyzed? Big data experts should know the basics of different data types and structures, for example binary search trees, red-black trees or hash tables. A basic understanding of algorithms is also important in order to analyze the data for a problem.

Database skills
Where there is a lot of data, there is also SQL - an absolute recommendation for the know-how list. But relational SQL databases are increasingly reaching their limits. This is where NoSQL databases come into play, which can also be used to store unstructured data.

Mathematical and statistical basics
Quantitative methods are also helpful for big data specialists. Basic knowledge of mathematics (especially linear algebra and multivariable infinitesimal calculus) and statistics help here. Even with appropriate software solutions such as SAS, Matlab or SPSS data scientists should know their way around.

Data visualization
In big data projects, it is often unavoidable to prepare the raw data visually in order to gain new perspectives on the information and gain new insights. When communicating about the work or results of a big data project at the latest, “colorful pictures” should not be missing. Because not every colleague or decision-maker can do something with pages of data evaluations. A big data expert must also have a feel for which form of presentation of the information is best: "classic" as a column, bar or pie diagram or in newer forms such as maps, heat maps or tree maps. You should also deal with appropriate tools for visualization, for example tableau or dygraphs.

Area of ​​activity of the company
This is less about specific skills that you can work off according to a list, but about thinking outside the box. Data scientists need to know and understand what a big data project is all about. This is where the employees of the departments help: What do the employees actually expect from a big data solution? What do the collected data say and how can they be evaluated? Which key figures are used for the evaluation?