1, Java

The essence of big data is nothing more than the calculation, querying and storage of massive amounts of data, the background development of the application scenarios that are easily accessible to large amounts of data access, so the Java language has a natural advantage, and now a lot of the components of the big data are developed in Java, such as HDFS, Yarn, HBase, MapReduce, ZooKeeper and so on.

2, Python

Python's advantage lies in the abundance of resources, with solid numerical algorithms, icons, and data processing infrastructure, and the establishment of a very good ecological environment. Not all companies can produce large amounts of data on their own for decision support, more Internet companies rely on crawlers to crawl the Internet data for analysis, and Python has a strong position in the field of web crawlers.Python's strategic positioning is to be a simple, easy to use but professional, rigorous combination of general-purpose language.Python syntax is simple and clear, and the underlying do a very good job of Python's syntax is simple and clear, and it encapsulates the underlying layers well, making it a high-level language that is easy to learn. What's more, Python is well packaged, composable, and embeddable, allowing you to wrap all sorts of complexity in Python modules and expose beautiful interfaces.

3. Scala

Scala runs on the JVM and basically successfully combines the functional paradigm with the object-oriented paradigm. It is currently making great inroads in the financial world and in corporate enterprises that need to process massive amounts of data.Scala typically takes a massively distributed approach to processing data, and it also powers big data processing platforms like Spark and Kafka, and is used by large enterprises like Twitter and LinkedIn.