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Learner Reviews & Feedback for ETL and Data Pipelines with Shell, Airflow and Kafka by IBM

4.5

stars

335 ratings

About the Course

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....

Top reviews

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It's one of the most challenging courses I've been enrolled!

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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.

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51 - 75 of 86 Reviews for ETL and Data Pipelines with Shell, Airflow and Kafka

By Ryan A M

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Jul 8, 2023

excelent course, thank you ibm.

By Albin C

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Oct 9, 2022

I it was a very good course!

By Md N S

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Jul 21, 2024

Excellent course

By Thọ L

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Aug 7, 2024

Great course!

By Burhanudin B

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Aug 9, 2023

Best tutorial

By Olabode A

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Oct 20, 2022

Nice course.

By Swati P

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Jul 11, 2024

Great Course

By Sai T Z

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Aug 7, 2023

Great course

By Sabeur M

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Jan 20, 2024

Great Cours

By Tasic D

•

Mar 31, 2023

Top Course

By Yitagesu S

•

Jun 24, 2024

I CANT PAY

By Mohib A

•

Mar 24, 2023

great

By Thanh N

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Oct 2, 2024

Good

By FREDERICK G

•

Aug 17, 2023

4.5

By Vishal S

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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.

By Markus Z

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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.

By prahal m

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Mar 24, 2023

it was good course should have also given an information on industry related solution and they can implement the same.

By Hector T

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Apr 1, 2023

Course offers valuable conceptual content but labs could be improved. Coursera assessment system is really poor.

By David R

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Jun 4, 2022

Good introduction to Airflow and Kafka however only one airflow operator is explored

By MESSOLO O

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Mar 30, 2023

Very good , Ilearn may thing and very good redaction and explains

By Otto Z g

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Jun 22, 2022

It takes 1 hour to connect the lab and start the service.

By Rafael M S

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Aug 24, 2023

Very helpful course!

By Mbaye B

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May 14, 2022

interesting

By Juan D G B

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Jan 15, 2023

good course

By Krishna k K

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Apr 12, 2022

good