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Data Engineering Course (1): Installing Big Data Hadoop Directly

Name: Data Engineering Course (1): Installing Big Data Hadoop Directly
Price: 55000 KRW

Students who want to learn Hadoop and Big Data will celebrate the amazing advancements in experiencing the world of Big Data through this course!

(4.6) 36 reviews

562 learners

hadoop3bigdata

hadoop

하둡

빅데이터

실무로배우는빅데이터기술

맵리듀스

Big Data

Hadoop

Data Engineering

Java

mapreduce

Reviews from Early Learners

What you will learn!

Encountering big data technology in everyday life
Handling Big Data with Hadoop
Learn distributed processing technology for handling big data with Hadoop
Handling Hadoop Big Data Using Java Language
Learn techniques to overcome the limitations of relational data processing with Hadoop
Learn about Hadoop's various projects and interfaces.

It's the era of big data! 👨‍💻
Become an expert with Hadoop.

The center of data science,
Hadoop is the trend!

Many IT giants, social media services, and others are competing to use Hadoop (Apache Hadoop) for big data analysis and processing. Hadoop is a Java-based framework designed to process massive amounts of data at low cost, enabling distributed storage and processing of large data sets. But what if you could achieve the level of a big data expert through Hadoop?

Through data analysis, companies will be able to pioneer new markets, create unique value, and provide new consumers with the thrill of real-time access to essential information. Big data is also a crucial skill for small and medium-sized businesses, so this is welcome news for those seeking employment or a career change in big data .

BigData with Hadoop

Google, Yahoo, Facebook, IBM, Instagram, Twitter, etc.
It is being used by many companies for data analysis.
Through Hadoop, a representative big data solution
Let's build a big data distributed system infrastructure .

This course begins with an understanding of big data terminology and then provides an indirect experience of handling big data using the open source software Hadoop . Through this course, students will simultaneously experience the world of big data technology and the Fourth Industrial Revolution.

What is Hadoop?

Hadoop is open source software that anyone can use for free.
In this lecture, we will cover big data using Hadoop version 3.2.1 .

Understanding big data
How to use Hadoop
OK at once.

_{Big data
About the term
Essential Understanding}

_{Hadoop 's
In concept and use
Introduction to Korea}

_{Through Hadoop
Big data processing
Learning Tutorials}

I recommend this to these people!

Of course, those who don't fit this category are also welcome. (Beginners are doubly welcome ✌)

_{Employment/Job Change
Future IT in consideration
Data science aspirants}

_{Via Java/Python
I want to deal with big data
Those who do it}

_{With interest and curiosity
About big data
Anyone who wants to experience it}

_{Hadoop 3.x version
Data environment, etc.
Office worker who wants to experience}

Before taking the class, please check your knowledge!

Prerequisite knowledge is the basics of the Java programming language, knowledge of big data and virtual machine/dataset related terminology , and a basic understanding of Linux Ubuntu .

The following content
I'm learning.

1. Understanding virtualization technology challenges and guest operating systems

We will learn virtualization technology, which is advantageous for server consolidation, and how to isolate multiple servers with a single OS through OS-level virtualization. Anyone can take on the challenge of creating and operating a large number of servers using Ubuntu, an open-source solution that supports Linux virtualization. Furthermore, we will gain knowledge of guest operating systems and gain extensive technical experience in distributing big data across multiple servers. Using server virtualization, you can experience multiple operating systems running on a single physical server or operating system in a highly efficient virtual machine.

Learn about the definition of Big Data and its practical applications.
Let's understand the terminology related to Hadoop, the data processing software preferred by businesses.

_{Data Sizes}

_{The Landscape: Big Data}

2. How to install Hadoop on Ubuntu 20.04 LTS and use commands

We'll cover the basics of using the Linux CLI (Command Line Interface) tools that front-end developers naturally encounter when developing web applications, and then seamlessly transition to the Linux terminal for Hadoop. Furthermore, we'll cover the basics of using Ubuntu in a non-Windows GUI environment, moving beyond a basic understanding of Linux systems like shell configuration files and moving towards an intermediate level.

Let's install and set up Linux (Ubuntu 20.04 LTS) as a virtual machine on a Windows 10-based laptop.
Install Hadoop version 3.2.1 on a Linux virtual machine.

_{Hadoop 2.x Architecture}

_{Hadoop 2.x vs. 3.x}

3. Hadoop 3.2.1 Latest Direction Guide & Understanding the Core Architecture Structure

The starting point for big data processing for unstructured data is understanding the Hadoop Distributed File System (HDFS), a model of Google's file system, MapReduce, and YARN for cluster scaling and resource management. We will examine the architectural structures of Hadoop versions 1, 2, and 3 one by one, providing students with a visual representation of the history of Hadoop technology.

Understand and integrate with the Hadoop Distributed File System (HDFS).
Understand the principles of the Map/Reduce framework and analyze data based on it.

_{HDFS Architecture}

_{YARN Core Components}

4. HDFS Shell Operation Guide and Building MapReduce Applications with Java/Python

While data manipulation techniques vary, the foundation of big data analysis lies in building MapReduce applications. From a basic wordcount MapReduce application in Python to a COVID-19 application built using Eclipse-based Java, building a variety of big data MapReduce applications is no longer an option; it's a necessary step forward.

Let's connect Hadoop with Java and implement an application.
Let's connect Hadoop with Python and implement an application.

_{Python Map/Reduce WordCount Application}

_{Java Map/Reduce WordCount Application}

Expected Questions Q&A!

Q. What is big data? Is its definition necessary when using Hadoop?

Yes, of course, when working with Hadoop, a brief definition and understanding of big data is required. Of course, this doesn't require a complete and in-depth understanding. However, it does require a level of understanding essential to working with Hadoop.

Big data involves handling extremely large datasets using Hadoop tools. These datasets serve as the foundation for analyzing numerous patterns and trends across numerous businesses. They are closely linked to human social behavior, patterns, and the value creation that occurs through interactions.

_{Image Source: TechTarget (Go to original article)}

Q. What is Hadoop? What are its components? What is the Hadoop stack?

Data from large-scale social sites, ranging from terabytes to petabytes (Zettabytes) Hadoop is helping with this mission. The Hadoop Stack refers to an open-source framework for handling big data.

Simply put, "Hadoop" is called the "Hadoop stack." Hadoop and the Hadoop stack help you build clusters using inexpensive, common commodity hardware and handle large-scale processing within these massive clusters of servers. The Hadoop stack, also known as "simple batch processing," is a Java-based "distributed computing platform." It allows individuals to batch process as much data as they want, periodically, distributing the data in the desired format to produce results.

Q. Is programming knowledge required?

Even if you have no programming knowledge or coding experience, it's okay. I teach with a deep understanding of Java and Python, as if you were experiencing them for the first time. While the lecture documentation is in English, I'll teach in Korean to ensure you can follow along without any difficulties. While I occasionally provide explanations in English, I believe anyone with a high school level will be able to interpret them. (Just like I achieved my dream, even with my limited English skills.)

Q. How relevant is big data to Hadoop?

This course naturally covers Hadoop. Beyond simple RDMSs like Oracle, MSSQL, or MySQL, it aims to address essential business requirements, starting with large-scale processing, data processing speed, and cost-effectiveness. Specifically, Hadoop addresses not only structured data—the relational data handled by row- and column-based RDMSs—but also unstructured data, such as images, audio, and word processing files themselves.

When dealing with service structure data, we're talking about data related to communication and data integration with web servers, such as email, CSV, XML, and JSON. HTML, websites, and NoSQL databases are also included. Of course, the accumulation of datasets used to handle computer-to-computer transfers of business documents, known as EDI, also falls into this category.

_{Image source: MonkeyLearn Blog (Go to original article)}

Q. What level of content is covered?

This course will guide users through the installation of Hadoop 3.2.1 on Ubuntu 20.04 LTS. Even if you have no prior Unix or Linux experience, you'll naturally learn installation techniques and the Linux operating system. Beyond the basics of Hadoop's CLI and user language, this course will also help you become familiar with Google's proprietary DFS and MapReduce technologies. Your understanding of YARN will be limited to basic theory. We anticipate a more in-depth study of YARN as you install a cluster in the Hadoop 3.3.0 intermediate course.

Q. Is there a reason you are using Ubuntu 20.04 LTS as a practice environment?

Ubuntu is free to use, and its LTS (Long-Term Service) program targets companies seeking long-term service support. By installing Hadoop on Linux, you can naturally build the operating system and development environment your business needs. By supporting the use of Eclipse and Intelligent within the same environment, you can contribute to realizing the dream of data science, which involves big data, right now.

_{Ubuntu is a Windows operating system that allows installation and operation.
Similar environment, i.e. GUI (Graphical User Interface)
We are helping users through the environment.}

Recommended for
these people

Who is this course right for?

Enthusiastic students who want to learn the basics of big data from scratch
For those who are thirsty for big data principles and applications
For those who want to learn Hadoop to handle big data in their companies
For those who have basic knowledge of Java

Need to know before starting?

The Concept of Big Data (Understanding Big Data)
Virtual Machine
Data set terminology
Understanding Linux (Ubuntu)
Java 15

Hello
This is

583

Learners

Reviews

Answers

4.6

Rating

Courses

네오아베뉴 대표 빌리 리 입니다.

2022년 9월 한국에 가족 모두 귀국한 뒤 현대자동차 빅데이터 프로젝트에 TA 컨설팅 (2022.09 -11월까지)하였고, 에자일 PM과 빅데이터 C-ITS 시스템 구축으로 하둡 에코시스템 및 머신러닝 딥러닝 리드하여 프로젝트 관리자 (PMO)역할을 하였습니다. 이후 Azure Data Factory & Azure Databricks 을 가지고 데이터 관리 기술을 AIA 생명 보험 이노베이션 데이터 플랫폼 팀에서 근무하면서 데이터 과학자로 깊은 탐구와 열정을 불살랐습니다.

2012년에서 2020년 까지 센터니얼 칼리지 Software Eng. Technician 졸업한 열공생이자 한국에서는 9년의 IT 경력 소유자로 금융권 (재무, 금융 프로젝트 및 빅데이터 관련 ) 에 다수 근무했습니다.

1999년 필리핀 (Dasmarinas) 지역에서 P.T.S. 네트워크 엔지니어링 자원 봉사자로 1년 근무하면서 글로벌 IT 세계와 네트워크 지식을 쌓으며 이후 2000년 한국으로 돌아와 K.M.C.에서 Clarion 4GL 언어로 Warehouse Inventory Control and Management 그리고 PIS Operational Test PCS C/C++ 개발했었습니다.

2001년 LG-SOFT SCHOOL 자바 전문가 과정 이수 후 CNMTechnologies 에서 e-CRM/e-SFA R&D 연구 및 개발 2년 정도 (한국산업은행/대정정부청사/영진제약) 다양한 프로젝트를 섭렵하였습니다.

2004년부터 2012년 캐나다로 올 때까지 SKT/SK C&C (IMOS), SC제일은행(TBC), 프로덴션 생명(PFMS), 교보생명 AXA Kyobo Life Insurance Account Management, Kook-min Bank 국민은행 Financial Management Reconstruction NGM외 다수 프로젝트에 참여 개발 및 리드하였습니다.

2012년 연말에 캐나다에 거주하면서 세 아이의 아빠이자 Scrum Master로서 에자일 개발 방식을 채택하여 핸디맨 어플/이커머스 어플/프로덱트 개발/레시피 어플 개발한 미주 캐나다 지역의 실경험자입니다.

Curriculum

All

85 lectures ∙ (6hr 39min)

Course Materials:

Lecture resources

Section 1. What exactly is big data?

3 lectures ∙ (13min)

Section 2. Why is Big Data technology important for companies?

2 lectures ∙ (5min)

Section 3. Hadoop's birth and background

3 lectures ∙ (12min)

Section 4. What are the four basic core modules (components) of Hadoop used for?

5 lectures ∙ (22min)

Section 5. HDFS (Hadoop Distributed File System) - Internal Architecture of Hadoop's Core Technology

7 lectures ∙ (39min)

Section 6. Hadoop General[1] : Hadoop Installation [1] - Understanding and Installing Virtual Machines

5 lectures ∙ (6min)

Section 7. Hadoop General[2] : Hadoop Installation[2] - Installing Hadoop 3.2.1

5 lectures ∙ (26min)

Section 8. Hadoop General[3] : Hadoop File System Shell Commands (Hadoop File System Shell Commands)

6 lectures ∙ (28min)

Section 9. Hadoop General[4]- Hadoop Common Commands (Hadoop Common Commands)

2 lectures ∙ (14min)

Section 10. YARN's Idea (MR3 on YARN) : MapReduce

3 lectures ∙ (9min)

Section 11. YARN THE IDEA (YARN THE IDEA) : YARN (Yet Another Resource Negotiator)

7 lectures ∙ (32min)

Section 12. MapReduce Tutorial (Basic Course) : MapReduce Tutorial Series (Basic Course) - Implementing MapReduce with the example Wordcount provided by Hadoop (MapReduce Wordcount Application)

3 lectures ∙ (10min)

Section 13. MapReduce Tutorial (Beginner's Guide) : Implementing MapReduce in Python - MapReduce Tutorial Series ( Intermediate Course) - Implement Mapper and Reducer in the Wordcount MapReduce Application

7 lectures ∙ (43min)

Section 14. MapReduce Tutorial (Intermediate Course) : MapReduce Tutorial Series with Java Language (Expert Course) - Setting up Hadoop Environment and Java in Eclipse

3 lectures ∙ (25min)

Section 15. MapReduce Tutorial (Intermediate) : MapReduce Tutorial Series with Java Language (Expert Course) - Taming Java Coding for MapReduce Implementation

9 lectures ∙ (41min)

Section 16. MapReduce Tutorial (Advanced): Practical Implementation of ebook WordCount Map and Reduce Stages in Java

7 lectures ∙ (30min)

Section 17. MapReduce Tutorial (Advanced Course)

8 lectures ∙ (37min)

Published:

Last updated:

Reviews

All

36 reviews

4.6

36 reviews

hadoop3bigdata
Reviews 3
∙
Average Rating 5.0
5
93% enrolled
この講義は、ビッグデータを扱うHadoopの専門家として養成したい心で講義を制作しました。クラウデラなどの包括的なオンプロメス配布ソフトウェアアプリケーション（OPD）を使用するのではなく、Hadoopを最初からインストールし、データセットを抽出、移動、およびロードすることに進みます。 1.xバージョンから始まったHadoopは、3.3バージョンまで多くの機能が追加されて非常に海賊なプラットフォームになりましたが、多くのツールを扱い、ビッグデータ専門家として養成される心溢れる講義になることを願っています。
kentucky8612311057
Reviews 4
∙
Average Rating 5.0
5
100% enrolled
利点： Hadoop MapReduceの基礎を学ぶことができます。韓国語で唯一のHadoop講義のようです残念なこと：マッパーを2つ使用して1つの共通キーに抽出するキーを2つ書く場合、コンパレータを直接設定する方法など気になった内容がなくて残念だった。欠点：講師様韓国語の発音が明確ではないが、背景音楽が大きくて何度何を言うのか再び聞かなければならなかった。 --------------------------------------- 先生の回答を見て別点5に修正します。
- hadoop3bigdata
  Instructor
  親切に詳しい評価ありがとうございます。更新した講義もあるので静かな時間に聞きながらハドゥプ専門家に残すことを楽しみにしています。
seaking79727
Reviews 38
∙
Average Rating 4.7
5
59% enrolled
Hadoopの入門者にはいいですね。本を見る前にまず学習するのにちょうどいいようです。
- hadoop3bigdata
  Instructor
  はい、良い評価ありがとうございます。 YARNアプリケーションを実行することを強調しました。ありがとうございます。楽しみにしています。
dlgnsxo1239897
Reviews 56
∙
Average Rating 5.0
5
100% enrolled
Hadoop川の本当によかったです！スパーク講義も開いてほしいです。ありがとうございます！
- hadoop3bigdata
  Instructor
  この講義を通して、Hadoopをもっと親しみ、きっかけになることを楽しみにしています。
jason
Reviews 28
∙
Average Rating 5.0
5
31% enrolled
講師様こんな質の良い講義をお手頃な価格で視聴できるように提供していただきありがとうございます！データエンジニアが新しくなりました HadoopやSparkなどのビッグデータフレワークをどのように始めなければならないのか悩み、厚い本を考慮していたときに過去に購入しておいたこの講義を見直すようになりました。 Javaをやったことがないので理解するのに少し時間がかかりますが、よく修了します！今年末や、来年初めにスパーク川の予定だと言うのにとても楽しみにしています！ pysparkベースでこの講義のように簡単に説明できることを願っています！

$42.90

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マイクロナットJavaクラウドアプリケーションの作成

hadoop3bigdata

Java Oracle Cloudネイティブアプリケーションを制作しながら、非同期処理、I/O処理、高性能Webアプリケーション、さらにJITコンパイラ技術で2倍以上の高速アプリケーション実行処理でネットワーク入力に負荷のない超高速アプリケーションを製作する予定です。

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半導体工程の職務への就職に向けた強力な武器、データ分析

pchaneui396975

熾烈で複雑な半導体プロセス分野の就職市場で、他の人が見過ごしやすい重要な力量を紹介します。

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Python, Big Data

半導体工程の職務への就職に向けた強力な武器、データ分析

pchaneui396975

枕投資法：寝ている間にお金を稼ぐAI株式自動売買マシン

cheatkeylab

AIで皆様の株式投資を革新しましょう！この講座で、ナスダック上位25銘柄をターゲットとした、実際に機能するAI自動売買システムをご自身で作成します。韓国投資証券APIで模擬投資を通じて経験とノウハウを積み、実際の口座でAI株式自動売買ができる特別な機会を提供します。株式初心者も問題ありません。株式の基礎概念と米国株をやるべき理由を分かりやすく解説し、全てのソースコードとリモートサポートまで提供します。これから先の60年間、私たちの人生において投資は必須です。皆様と共に歩むAI投資パートナーをご自身で作成してみましょう！

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Python, Deep Learning(DL), Data Engineering

枕投資法：寝ている間にお金を稼ぐAI株式自動売買マシン

cheatkeylab

[無料]基礎テキストマイニング：アプリレビュー分析 with Python（40分完成）

leejinkyu0612

この講義では、Pythonを活用したテキストマイニング分析に関する基礎理論と実習を学ぶことができます。実務や論文作成時に必要な基礎的なテキストマイニングデータ分析手法を説明します。

초급

Text Mining, Big Data, NLP

[無料]基礎テキストマイニング：アプリレビュー分析 with Python（40分完成）

leejinkyu0612

非専攻者のための数学統計基礎（実習）

medici

このコースは、Pythonを使用した機械学習の数学と統計の基本的な内容を実践するプロセスです。機械学習とディープラーニングにはプログラミング能力も必要ですが、原理を理解するために数学的背景が必要です。このプロセスにより、人工知能に必要な基礎数学をじっくり身につけることができます。

초급

Linear Algebra, Probability and Statistics, Python

非専攻者のための数学統計基礎（実習）

medici

資格取得と実務！一度に経営情報視覚化（実技）Tableau

onepm

この講座は、主催機関の出題ガイドに基づき、経営情報可視化実技試験（Tableau）に必須の核心となる概念と機能を体系的に習得することを目標としています。実戦で頻繁に出題される機能を自由に活用できるように実習中心に構成しており、模擬試験A, Bタイプの問題演習を通じて出題類型への理解と問題解決能力を共に向上させることができます。講義案および講義資料は、https://github.com/hansikyung/Tableau_OneTime で確認できます。

초급

Big Data, Tableau

資格取得と実務！一度に経営情報視覚化（実技）Tableau

onepm

Rを活用したビッグデータ及び統計分析

kpcre

Rプログラミングを利用して、誰でもビッグデータ分析ができるように、データの基本的な概念、Rの有用な関数とパッケージ、データ分析の実習を盛り込みました。

입문

R, Big Data

Rを活用したビッグデータ及び統計分析

kpcre

Pythonを活用したビッグデータ分析と可視化

kpcre

ビッグデータ分析に必要なコアPython文法を確認し、さまざまな例を通じてビッグデータファイル処理および視覚化方法などを提示しました。

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Python, Big Data

Pythonを活用したビッグデータ分析と可視化

kpcre

確率と統計 101

루비네코딩

いよいよ皆のための確率と統計講義が来ました！理論とコーディング実習を通じて、次々と確率と統計の原理について学びます。

초급

Probability and Statistics, Big Data, Python

確率と統計 101

루비네코딩

ADsPクイックスタート！

루비네코딩

ADsP認定資格合格戦略を学び、暗記科目をまとめて学びます。これ以上躊躇しないでADsP資格に挑戦してください！

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Big Data, ADsP

ADsPクイックスタート！

루비네코딩

Data Engineering Course (1): Installing Big Data Hadoop Directly

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What you will learn!

The center of data science, Hadoop is the trend!

Understanding big data How to use Hadoop OK at once.

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The following content I'm learning.

1. Understanding virtualization technology challenges and guest operating systems

2. How to install Hadoop on Ubuntu 20.04 LTS and use commands

3. Hadoop 3.2.1 Latest Direction Guide & Understanding the Core Architecture Structure

4. HDFS Shell Operation Guide and Building MapReduce Applications with Java/Python

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OK at once.

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