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This article shows you how to analyze the full set of Spark knowledge system, the content is concise and easy to understand, it will definitely brighten your eyes. I hope you can get something through the detailed introduction of this article.

Based on a set of unified data model (RDD) and programming model (Trans-foration / Action), Spark constructs Spark SQL, Spark Streaming, Spark MLibs and other branches, and its functions cover many fields of big data. As a rising star and natural advantage, Spark has become the most popular distributed memory computing engine in the open source community.

At the same time, as a unified analysis platform that supports both big data and artificial intelligence, Spark has become the most popular big data computing framework for enterprises with its advantages in data integration, flow processing, machine learning, interactive analysis and so on.

It can be said that whether you are an engineer big data or an algorithm engineer such as machine learning, Spark is a computing engine that you must master.

Technical people who have mastered Spark have become popular in the market, but many beginners want to learn about it but do not have a series of comprehensive entry methods.

Don't worry, here is a study video worth 1788 yuan of "Spark complete knowledge system" carefully polished by Liao Xuefeng and other technical experts for three months. This material will be especially suitable for people who want to promote or change careers in jobs such as Java, PHP, operation and maintenance, or want to work related to big data.

Give it to everyone free of charge for limited time! Scan the QR code below and get it, and the hand moves slowly.

Scan the QR code below

Time-limited and free of charge

You can make an appointment to pick it up by scanning the code of Wechat.

(the value of the data depends on what you do after you get it. Don't be a collector.)

What can be obtained from this information?

After watching this video, you will gain:

1. Deeply understand the function-oriented programming language Scala to develop Spark programs.

2. Deeply analyze the characteristics of RDD, the underlying core of Spark.

3. Deeply understand the caching mechanism and broadcast variable principle of RDD and its use.

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

More importantly, by learning the knowledge content of this video, it will provide strong support for your later job and interview.

What do you have in this file?

1. The memory computing framework of Spark-- course content introduction.

Knowledge points: spark's pre-class preparation

2. An introductory case of developing Spark through IDEA tools

Knowledge points: maven to build scala project

3. Spark's in-memory computing framework-- an introductory case of developing Spark through IDEA tools-- code development

Knowledge points: scala grammar, spark program development

4. Spark's memory computing framework-- the program is packed into jar packages and submitted to the Spark cluster to run.

Knowledge points: program into jar package, spark-submit to submit the use of task commands

5. The memory computing framework of Spark-- what is the RDD of the underlying programming abstraction of Spark?

Knowledge points: Spark underlying core RDD

6. Spark's in-memory computing framework-- five features of RDD abstracted by Spark underlying programming.

Knowledge points: the characteristics of the underlying core RDD of Spark

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

Knowledge points: an in-depth analysis of the five characteristics of the underlying core RDD of Spark

8. Operator operation classification of Spark underlying kernel RDD.

Knowledge points: operator classification of spark underlying kernel RDD

9. Dependency of RDD, the underlying core of Spark

Knowledge points: spark underlying core RDD dependencies (wide and narrow dependencies)

10. The caching mechanism of Spark underlying core RDD

Knowledge points: spark underlying core RDD caching mechanism, application scenarios, how to use, how to clear the cache

11. Construction and partition of DAG directed acyclic graphs stage

Knowledge points: DAG directed acyclic graphs and partition stage

12. Analyze the process of submitting, dividing and scheduling Spark tasks based on wordcount program.

Knowledge points: spark task submission, division, scheduling process analysis

13. A case study of clickstream log analysis through Spark development

Knowledge points: RDD's common operators count/map/distinct/filter/sortByKey use

14. Realize the case of ip attribution query through Spark development-- requirements introduction

Knowledge points: ip attribution query requirements description

15. Ip attribution query case-code development is realized through Spark development.

Knowledge points: broadcast variables in spark, ip address conversion to Long type numbers, binary query

The above content is how to analyze the complete knowledge system of Spark. Have you learned the knowledge or skills? If you want to learn more skills or enrich your knowledge reserve, you are welcome to follow the industry information channel.

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