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Data Engineering

Big Data Pipeline Master; Tools and Techniques for Success

You will learn about the four stages of big data processing [data collection ▶ data storage ▶ data analysis ▶ expression] in a more fun and systematic way through a code lab method consisting of 30% theory + 70% practice 🧑🏻‍🏫

(5.0) 18 reviews

314 students

Big Data
Elasticsearch
Apache Spark
Kibana
Hadoop
Thumbnail

This course is prepared for Basic Learners.

What you will learn!

  • Bigdata Processing Pipeline

  • Data Acquisition

  • Data Storage

  • Data Visualization

  • Data Analysis

  • Hadoop Distributed Filesystem

  • Spark Apache

  • ELK Stack (Elasticsearch)

Learn in a hands-on, hands-on manner
Master the Big Data Pipeline!

special thanks to my lovely students 👨🏻‍🎓 * appreciate it, believe you'll do well anywhere 👩🏻‍🎓



Data Processing Theory and Practice
The core of the big data pipeline !

Hello, this is J.PHIL 🙇🏻
Taking advantage of this great opportunity, Inflearn will be offering its first lecture , 'Data Processing Theory and Practice' , for beginners interested in building and analyzing big data systems.

Key words at a glance

Mastering Big Data Processing: Tools and Techniques for Success

Distributed System
Apache Spark
HDFS
Elasticsearch
Logstash
Kibana
Crawler
Scraping
Selenium
AWS S3
Node.js
Docker

Why are we taking this course ?
Should I listen? 📚

Due to the rapid technological advancements of the past 10 years or so, various platforms and services have been created, and various customers are living a high-quality life on them. In response, many companies are discovering and extracting valuable data from the data that is being generated in a hurry, and designing BM (Business Model) to provide us with a more valuable life.

In this environment, what and how should our engineers👷🏻 prepare if they “dare to predict and respond to the future”? They need to cultivate the ability to manage and handle data . Conversely, if you can handle data well and express it well, what can you contribute to the industry?

Data-driven decision-making

💡 Big data analytics enables organizations to make data-driven decisions, which in turn improves business outcomes.

Increased efficiency and productivity

💡 Big data analytics can help organizations streamline operations, reduce costs, and increase productivity.

Innovation

💡 Big data analytics can drive innovation by enabling companies to develop new products and services, improve existing products and services, and create new business models.


So how is this lecture?
Is it composed ? 📑

📝

Experience of writing a paper for a Data Top-Tier Conference

👨🏻‍💼

Valuable experience in building and analyzing big data systems gained in the field

🧑🏻‍🏫

Long experience in nurturing good students at university

Based on this valuable experience, I have structured the lecture into a broad yet informative course of 14 weeks or more on the four stages of the big data process to serve as a good starting point for “anyone interested in this field” 📚

You will learn about data collection ▶ data storage ▶ data analysis ▶ expression using the technologies introduced above in a code lab format of 30% theory and 70% practice . We have reflected and reflected on the valuable feedback from excellent students for about 6 years, and have organized the lectures with the easiest and highest quality content possible, so it will definitely be of great help to beginners.

Oh! For your reference, the lecture materials were written in English as much as possible so that you can find various references later or use this opportunity to help you in the research field or in a better company. 🧗🏻‍♀️


What do we learn? 🧑🏻‍🏫

Based on the Big Data Processing 4 Steps above, the curriculum is structured as follows. (Refer to the free video for Week 1)

Interested in big data pipelines?
Anyone can take the class 🧑🏻‍🎓

Anyone who knows Python, Linux commands, and basic knowledge of databases can take the course.

[Promotion] We support the cost of lectures for students and job seekers 💪

For students or job seekers without income We offer a discount of about 20% . Please apply through the link below and for smooth communication, please leave a log in the inquiry tab before taking the class , such as "${Self-introduction} student/job seeker [promotion] applied" :)

Application link / Pre-course inquiry

[Promotion] +200 students commemoration, extended promotion period💪

We are offering a 3-month free extension promotion for new students, excluding students who received the +100 person promotion. Please apply through the link below :)

Application Link

What is the practice environment like? 💻

If you prepare a stress-free environment as shown below, you will be able to follow the class.
(The actual cluster configuration will be covered in the [Beginner] theory lecture currently in production 🙏🏻)

  • OS: Ubuntu or Linux
  • Machine specifications
    • Aws t2.medium 2 Core 4GB // ec2 free.tier attendance is possible
    • You can attend with the above OS using Virtualbox

[Beginner DOCKER Course] Promotion Event 😄

For those who want to learn more about Docker, I highly recommend [Mastering Docker and Dockerizing for Beginners]. We will apply a promotion* to those who have taken [Mastering Big Data Pipelines].

[Big Data Cluster Construction Package] Launch Promotion Event 🎓

This is recommended for those who want to build a big data cluster with high availability using a solid code lab. Click on the lecture link on the side and leave a message in the [ Pre-course inquiry ] section saying "ID / Email / Promotion application."


Who created this course
Introducing J.PHIL ✒️

Recommended for
these people!

Who is this course right for?

  • University students in their 3rd and 4th year who want to take a course on big data processing systems

  • Non-major developers interested in a career in data

  • Junior developer interested in data collection, storage, analysis, and visualization

  • Job seekers preparing for a big data job interview

Need to know before starting?

  • Python Basic Coding

  • Basic knowledge of Linux commands

  • Database Basics

Hello
This is

407

Students

36

Reviews

50

Answers

4.9

Rating

2

Courses

안녕하세요 J.PHIL 입니다 🧑🏻‍🎓

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Curriculum

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57 lectures ∙ (14hr 9min)

Course Materials:

Lecture resources
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