Big data was something I had only vaguely felt, but after listening to the lecture, I began to understand the concept and imagine how to use it.

The instructor has good diction and the lecture curriculum flows well in sequence.
Thank you for the great lecture!

outlier

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 🧑🏻‍🏫

jphil

Big Data Pipeline Master; Tools and Techniques for Success

Hello, Developer Dev,

I am glad that you found it helpful as I have organized and lectured on the content that I have meaningfully experienced.
Please listen comfortably when you review later and let me know if you have any questions.

빅데이터

데이터처리

데이터분석

elasticsearch

여러분들은 빅데이터 프로세싱의 4단계에 해당하는 [데이터 수집 ▶ 데이터 저장 ▶ 데이터 분석 ▶ 표현] 에 대한 내용을 이론 30% + 실습 70%으로 이루어진 코드랩 방식으로 보다 재밌고 체계적으로 학습합니다 🧑🏻‍🏫

J.PHIL

입문

초급

중급이상

Big Data

일래스틱서치

Elasticsearch

아파치 스파크

Apache Spark

키바나

Kibana

아파치 하둡

Hadoop

빅데이터 파이프라인 마스터; 성공을 위한 도구와 기술

Introduction to Big Data

Data Acquisition [Basic]

Data Acquisition [Advanced]

[Session] Git and Github

Data Storage [S3]

Data Storage [HDFS] 이론

Data Storage [HDFS] 실습

Data Analysis [Spark Basic]

Data Analysis [Explore Dataframe]

Data Analysis [Spark Advanced]

Data Visualization [NODE JS] 이론

Data Visualization [NODE JS] 실습

[Session] ELK 1, Elasticsearch

[Session] ELK 2, Kibana and Logstash

Special Lecture

Special Lecture <Docker>