If you are self-taught, it is difficult to reach professional level. It is very good that the level of self-study can reach the level of introduction to big data. It is difficult to reach the level of a big data development engineer, so training is useful. If there is a special big data training teacher to teach, you will get effective counseling when you encounter difficulties, and the learning atmosphere is different.

Participating in big data training classes can improve learning efficiency. Self-study is inefficient and easy to use the wrong learning method. People who participate in big data training are basically zero-based or a little-based, and the perfect curriculum system of training institutions can meet their learning needs and work needs.

Big data skills are updated very quickly. In recent years, a version has been updated every year. Most of the information skills that ordinary people come into contact with may be eliminated knowledge. Course content is an important capital for the survival and competition of training institutions, and the course content of reliable big data training institutions is constantly updated.

What are the big data trainings?

Big data is one of the popular vocational skills of college students. The main training contents include basic knowledge of Java, avaEE core technology, Hadoop ecosystem, Spark ecosystem and big data analysis. Big data training is one of the most popular courses, and big data training and learning has become the general trend.

Java Foundation: This is the basic knowledge of learning big data, including Java basic grammar, object-oriented programming, common classes and tools, collection framework system, exception handling mechanism, files and IO streams, mobile account opening management system, multithreading, enumeration and garbage collection, reflection, new features of JDK, and address book system.

JavaEE core: The main training contents include front-end technology, database, JDBC technology, server-side technology, Maven, Spring, SpringBoot and Git.

Hadoop ecosystem: The main contents include Linux, Hadoop, ZooKeeper, Hive, HBase, Phoenix, Impala, Kylin, Flume, SQOOP &；; DataX, Kafka, oozie & Azkabanhue, intelligent agricultural warehouse analysis platform.

Spark ecosystem: The main contents include Scala, Spark, traffic monitoring project and Flink.