Spark's complete knowledge system is finally here!

Spark 全套知識體系，終於搞到了！

Posted by Spark on 2020-03-01 09:00:00

This year, many people feel that promotion in the workplace is not so smooth, and it is true that it is caused by the environment. However, some people around them are able to jump job appreciation in such a "difficult" environment. The reason is nothing more than taking precautions and going forward.

Take the current hot big data. With the hot of artificial intelligence, it is estimated that the talent gap of big data will reach 1.5 million in the next three to five years (data from the "Big Data Talent Report" data). The high scarcity of talent means high returns, so under the same working years, the salary of big data engineers is generally higher, and the salary increase will also exceed other positions. It 's a good time to enter big data now .

 

Speaking of big data development, technical operation threshold or ratio higher: because of the need to master Hadoop, Presto and Spar k and other technical points,  coupled with big data development technology update soon, the first few years only H adoop knowledge, Spark was Just rise, Spark is 3.0 in a blink of an eye. It is not easy to learn big data through books or online materials, and it is often impossible to catch the point.

 

Sp ARK as to support big data and artificial intelligence unified analysis platform, big data points analyzed in the most affected Huan analysis tool welcome, not only profound, but also developing rapidly. Spark grasp it is to master the big data era veins stroke, large data development engineer who , palm grip S Park can be said to critical.

 

But because the Spark technology stack is relatively complex and its functions are both powerful and rich, it is especially difficult to master. Many people who are new to big data development are confused at first. They don't know where to start and how to go from nothing to become a master of Spark.

 

Do not worry, there are one from the Liao Xuefeng, and many other technical experts two months lasted carefully polished the value of 1788 yuan of " Spark Spark underlying core RDD and internal framework to explain the depth of the principle of " learning video, for in Java, PHP, operation and maintenance, etc. This material will be especially suitable for people who want to improve or change careers , or want to work on big data . Now Xiaobian has applied for 128 free places for everyone .

 

Long press the scan code twice to add WeChat

Free for the top 128 only

Liao Xuefeng's original video material worth 1788 yuan

 

 

 

Screenshot Wechat scan code to add collection

(The value of the data depends on your actions after you receive it. Do not be a collector party.)

 

After watching this video, you will gain:

 

In-depth understanding of Spark programming for functional programming language Scala;

In-depth analysis of the characteristics of the underlying core RDD of Spark;

In-depth understanding of RDD's cache mechanism and broadcasting variable principle and its use;

Master the Spark task submission, task division, and task scheduling process.

 

More importantly, by learning the knowledge content of this video, you will provide strong support for your subsequent jobs and interviews . The contents of the study materials are as follows:

 

  1. Spark's in-memory computing framework-course content introduction

    Knowledge point : Preparing for Spark

     

  2. An entry case for developing Spark with IDEA tools

    Knowledge point : maven build scala project

     

  3. Spark's in-memory computing framework-an entry case for developing Spark with IDEA tools-code development

    Knowledge points : scala syntax, spark program development

     

  4. Spark's memory computing framework-the program is packaged into a jar package and submitted to the Spark cluster to run

    Knowledge points : the use of the program into a jar package, spark-submit submission task

     

  5. Spark's memory computing framework-what is the RDD of Spark's underlying programming abstraction

    Knowledge point : Spark's underlying core RDD

     

  6. Spark's in-memory computing framework—five features of Spark's underlying programming abstraction, RDD

    Knowledge : Features of Spark's underlying core RDD

     

  7. Analyze the five characteristics of RDD based on the word statistics case

    Knowledge points : An in-depth analysis of the five core RDD features of Spark's underlying core

     

  8. Operator operation classification of Spark's underlying core RDD

    Knowledge point : operator classification of the underlying core RDD of Spark

     

  9. Dependencies of Spark's underlying core RDD

    Knowledge point : dependency of the underlying core RDD of Spark (width and narrowness)

     

  10. Spark's underlying core RDD cache mechanism

    Knowledge points : The cache mechanism, application scenarios, how to use, and how to clear the cache of the underlying core RDD of Spark

     

  11. Construction and division stage of DAG directed acyclic graph

    Knowledge point : DAG directed acyclic graph and division stage

     

  12. Analyze the submission, division, and scheduling process of Spark tasks based on the wordcount program

    Knowledge : Anatomy of Spark task submission, division, and scheduling process

     

  13. Click Stream Log Analysis Case by Spark Development

    Knowledge point : RDD common operator count / map / distinct / filter / sortByKey use

     

  14. Cases of querying IP attribution through Spark development—requirements

    Knowledge point : IP address query requirements description

     

  15. Cases of querying IP attribution through Spark development-code development

    Knowledge points : broadcast variables in Spark, IP address conversion to Long number, binary search

说是大环境所致，这也没错。但身边有些人，却能在如此“艰难”的环境下，顺利跳槽升值。究其原因，无非就是未雨绸缪，顺势而上。

拿当下火热的大数据来说，伴随人工智能的火热，据且未来3到5年大数据人才缺口将达到150万之多（数据来自《大数据人才报告》数据）。人才的高度稀缺意味着高额回报，因此在相同工作年限的情况下，大数据工程师的薪资普遍更高，待遇涨幅也会超过其他岗位。现在入局大数据，会是一个不错的时机。

 

说起大数据开发，技术门槛还是比较高的：因为需要掌握Hadoop、Presto及Spark等多个技术点, 加之大数据开发技术更新快，前几年只需要Hadoop知识，Spark才刚刚兴起，眨眼间Spark都3.0了。想通过书籍或者网上资料学习大数据绝非易事儿，还往往抓不住重点。

 

Spark作为同时支持大数据和人工智能的统一分析平台，大数据分析里最受欢迎的分析工具，不但博大精深，而且发展迅速。掌握Spark就是掌握了大数据时代的脉搏，对大数据开发工程师来说，掌握Spark可谓至关重要。

 

但由于Spark技术栈相对复杂，它的功能既强大又丰富，因此掌握起来尤其困难。很多初入大数据开发这一方向的人，一开始一头雾水，不知道从什么地方开始下手，也不知道怎么样从一无所知到成为精通Spark的高手。

 

别急，这里有一份由廖雪峰等多位技术专家历时2个月精心打磨的价值1788元的《Spark底层核心RDD和Spark框架内部原理深度讲解》学习视频，对从事Java、PHP、运维等工作想要提升或转行，或想从事大数据相关工作等人群来说，这份资料将特别适合。现在小编为大家申请到了128个免费领取名额，手慢无~

 

长按扫码2次即可添加微信

仅限前128名免费领取

廖雪峰的原价值1788元的视频资料

 

 

 

截图微信扫码即可添加领取

（资料的价值取决于你领完后的行动，千万莫做收藏党）

 

看完本视频，你将收获：

 

深入理解面向函数式编程语言Scala开发Spark程序；

深入剖析Spark底层核心RDD的特性；

深入理解RDD的缓存机制和广播变量原理及其使用 ；

掌握Spark任务的提交、任务的划分、任务调度流程。

 

更重要的是，通过学习本视频的知识内容，对你后面的工作和面试将提供强大的支持。学习资料内容大概如下：

 

  1. Spark之内存计算框架—课程内容介绍

    知识点：spark的课前准备内容

     

  2. 通过IDEA工具开发Spark的入门案例

    知识点：maven构建scala工程

     

  3. Spark之内存计算框架—通过IDEA工具开发Spark的入门案例—代码开发

    知識點：scala語法、spark程序開發

     

  4. Spark之內存計算框架—程序打成jar包提交到Spark集群中運行

    知識點：程序打成jar包、spark-submit提交任務命令的使用

     

  5. Spark之內存計算框架—Spark底層編程抽象之RDD是什麼

    知識點：spark底層核心RDD

     

  6. Spark之內存計算框架—Spark底層編程抽象之RDD的五大特性

    知識點：spark底層核心RDD的特性

     

  7. 基於單詞統計案例來深度剖析RDD的五大特性

    知識點：spark底層核心RDD的五大特性深度剖析

     

  8. Spark底層核心RDD的算子操作分類

    知識點：spark底層核心RDD的算子分類

     

  9. Spark底層核心RDD的依賴關係

    知識點：spark底層核心RDD的依賴關係(寬窄依賴)

     

  10. Spark底層核心RDD的緩存機制

    知識點：spark底層核心RDD的緩存機制、應用場景、如何使用、如何清除緩存

     

  11. DAG有向無環圖的構建和劃分stage

    知識點：DAG有向無環圖和劃分stage

     

  12. 基於wordcount程序剖析Spark任務的提交、劃分、調度流程

    知識點：spark任務提交、劃分、調度流程剖析

     

  13. 通過Spark開發實現點擊流日誌分析案例

    知識點：RDD常見的算子count/map/distinct/filter/sortByKey使用

     

  14. 通過Spark開發實現ip歸屬地查詢案例—需求介紹

    知識點：ip歸屬地查詢需求介紹說明

     

  15. 通過Spark開發實現ip歸屬地查詢案例—代碼開發

    知識點：spark中的廣播變量、ip地址轉換成Long類型數字、二分查詢

 

Ref: https://mp.weixin.qq.com/s/r8SmiZrOzrlS_Dz6YGvbhg