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Everyone knows that big data pays high salaries and has good prospects. Big data requires Java infrastructure. For children who know a little Java, how to switch to big data? Today, Xiaobian gives you a specific learning roadmap for big data engineers. [ps: you can learn big data without java foundation]

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For Java programmers, the mainstream platform for big data, hadoop, is based on Java development, so Java programmers switch to the direction of big data development from the language environment more smoothly, and many application frameworks based on big data are also Java, so mastering Java language in many big data projects has certain advantages.

Here I still want to recommend my own big data learning exchange group:529867072, the group is learning big data development, if you are learning big data, Xiaobian welcomes you to join, everyone is a software development party, from time to time to share dry goods (only big data software development related), including the latest big data advanced materials and advanced development tutorials I collated myself, welcome advanced and want to go deep into big data small partners to join.

Of course, the core value of hadoop is that it provides a distributed file system and a distributed computing engine that, for most companies, does not require modification. In addition to familiarity with programming, you usually need to learn some knowledge of data processing and data mining. In particular, if you are moving towards a data mining engineer, you need to master more knowledge about algorithms.

For data mining engineers, although they also need to master programming tools, most of the time they use hadoop as a platform and tool, and use the interfaces provided by this platform and tool to perform data processing and data mining using various scripting languages.

Therefore, if you are heading towards data mining engineering, then proficiency in distributed programming languages such as scala, spark-mllib, etc. may be more important.

Java Programmer to Big Data Engineer Learning Roadmap:

Step 1: Distributed Computing Framework

Master hadoop and spark distributed computing frameworks, understand file systems, message queues and Nosql databases, learn related components such as hadoop, MR, spark, hive, hbase, redies, kafka, etc.;

Step 2: Algorithms and Tools

Learn various data mining algorithms, such as classification, clustering, association rules, regression, decision trees, neural networks, etc., and master a data mining programming tool: Python or Scala. Major platforms and frameworks already provide libraries of algorithms, such as Mahout on hadoop and Mlib on spark, and you can start learning these algorithms by learning these interfaces and scripting languages.

Step 3: Mathematics

Complementary mathematics: high numbers, probability theory and linear generation

Step 4: Project Practice

1)Open source projects: tensorflow: Google's open source library, there are more than 40000 stars, very amazing, support mobile devices;

2)Enter the data contest

3)Project experience through corporate internships

If you are only doing big data development and operations, you can skip the second and third steps. If you are focusing on applying existing algorithms for data mining, you can skip the third step first.

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