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学生对 IBM 提供的 ETL and Data Pipelines with Shell, Airflow and Kafka 的评价和反馈

4.5
433 个评分

课程概述

Delve into the two different approaches to converting raw data into analytics-ready data. One approach is the Extract, Transform, Load (ETL) process. The other contrasting approach is the Extract, Load, and Transform (ELT) process. ETL processes apply to data warehouses and data marts. ELT processes apply to data lakes, where the data is transformed on demand by the requesting/calling application. In this course, you will learn about the different tools and techniques that are used with ETL and Data pipelines. Both ETL and ELT extract data from source systems, move the data through the data pipeline, and store the data in destination systems. During this course, you will experience how ELT and ETL processing differ and identify use cases for both. You will identify methods and tools used for extracting the data, merging extracted data either logically or physically, and for loading data into data repositories. You will also define transformations to apply to source data to make the data credible, contextual, and accessible to data users. You will be able to outline some of the multiple methods for loading data into the destination system, verifying data quality, monitoring load failures, and the use of recovery mechanisms in case of failure. By the end of this course, you will also know how to use Apache Airflow to build data pipelines as well be knowledgeable about the advantages of using this approach. You will also learn how to use Apache Kafka to build streaming pipelines as well as the core components of Kafka which include: brokers, topics, partitions, replications, producers, and consumers. Finally, you will complete a shareable final project that enables you to demonstrate the skills you acquired in each module....

热门审阅

BN

Mar 30, 2023

Overall it's a good course. I wish I could use dos2unix, tr, or sed for removing ^M from the toll_data.tsv. The Final Assignment Instructions could have been clearer.

DS

Jun 13, 2022

Excellent introduction to this topics. Labs contain all you need to know how to start using this type of technologies. Highly recommended.

筛选依据:

51 - ETL and Data Pipelines with Shell, Airflow and Kafka 的 75 个评论(共 94 个)

创建者 Arnold J M

Oct 1, 2022

Great course with hands on labs for practices.

创建者 WONG L X

Aug 20, 2022

great course but airflow does not work on

创建者 Minh N T

Apr 12, 2022

Useful course for beginner Data engineer

创建者 Steven

Sep 27, 2025

great course with alot of hands on

创建者 Rafael R

Mar 5, 2025

Great course! I will recommend it!

创建者 Diwas A

Jan 25, 2024

Good course for intro level ETL !

创建者 Muhammad T K

Jul 8, 2022

Absolutely brilliant for starter

创建者 Ryan A M

Jul 8, 2023

excelent course, thank you ibm.

创建者 Albin C

Oct 9, 2022

I it was a very good course!

创建者 Md N S

Jul 21, 2024

Excellent course

创建者 Tho H L

Aug 7, 2024

Great course!

创建者 Burhanudin B

Aug 9, 2023

Best tutorial

创建者 Olabode A

Oct 20, 2022

Nice course.

创建者 Swati P

Jul 11, 2024

Great Course

创建者 Sai T Z

Aug 7, 2023

Great course

创建者 Sabeur M

Jan 20, 2024

Great Cours

创建者 Tasic D

Mar 31, 2023

Top Course

创建者 Yitagesu S

Jun 24, 2024

I CANT PAY

创建者 Jorge A c c

Nov 24, 2025

excelente

创建者 Mauricio M

Dec 7, 2024

Excelent!

创建者 Mohib A

Mar 24, 2023

great

创建者 Thanh N

Oct 2, 2024

Good

创建者 FREDERICK G

Aug 17, 2023

4.5

创建者 Vishal S

Nov 5, 2023

I got to learn in the most practical way. I loved the course and how they connected the learnings of different tech stacks in one. One thing they can improve is by providing more reading materials which can help us learn the advances since the course covers more of basic aspect of the technologies.

创建者 Markus Z

Mar 28, 2022

Good compact summary of the topics.

Regarding the assignment: Good to have an environment for testing your code directly. Unfortunatly it was a bit unstable. Final assignment was a bit to much screenshots and lesser coding.