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Big data, artificial intelligence technology leads the trend of science and technology, pushing open the door of big data era! The country likes it! The prospect of policy support is bright! Then, the talents who learn from big data are like crucian carp crossing the river. The overall situation is thriving! Here, good programmers send you technical information to help you learn big data skills, we must pay attention to the quality of training, only in this way, we can get twice the result with half the effort! Next, I will explain to you the three required courses for big data!

I. Hadoop ecosystem

Hadoop   is a distributed system infrastructure developed by the Apache Foundation. Users can develop distributed programs without knowing the underlying details of the distribution. Make full use of the power of the cluster for high-speed computing and storage. Hadoop implements a distributed file system (Hadoop   Distributed   File   System), referred to as HDFS.

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The Hadoop   "stack" consists of several components. These include:

1.Hadoop distributed File system (HDFS): the default storage layer for all Hadoop clusters

two。 Name node: in a Hadoop cluster, a node that provides data storage location and node failure information.

3.   secondary node: a backup of the name node, which periodically copies and stores the data of the name node in case the name node fails.

4. Job tracker: a node in a Hadoop cluster that initiates and coordinates MapReduce jobs or data processing tasks.

5. From the node: the normal node of the Hadoop cluster, stores data from the node and gets data processing instructions from the job tracker.

II. Spark ecosystem

Spark   is an open source cluster computing environment similar to   Hadoop  , but there are some differences between the two. These useful differences make   Spark   perform better in some workloads. In other words, Spark   enables in-memory distributed datasets to optimize iterative workloads in addition to interactive queries. Big data Learning Exchange Group: 251956502

Spark   is implemented in the   Scala   language, which uses   Scala   as its application framework. Unlike   Hadoop  , Spark   and   Scala   can be tightly integrated, where   Scala   can manipulate distributed datasets as easily as local collection objects.

Third, Storm real-time development

Storm is a free and open source distributed real-time computing system. Using Storm can easily and reliably handle unlimited data streams, just like Hadoop batch processing big data, Storm can process data in real time. Storm is simple and can be used in any programming language.

Storm has the following characteristics:

1. Programming is simple: developers only need to focus on application logic, and like Hadoop, the programming primitives provided by Storm are also very simple

two。 High performance, low latency: can be applied to ad search engines that require real-time response to the actions of advertisers.

3. Distributed: it can easily deal with scenarios with large amount of data and can not be handled by a single computer.

4. Scalable: with the development of business, the amount of data and calculation of   is increasing, and the system can be expanded horizontally.

5. Fault tolerance: the failure of a single node does not affect the application

6. Messages are not lost: guaranteed message processing

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