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IBM

ETL and Data Pipelines with Shell, Airflow and Kafka

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.

IntermediateCourse18 hours

Featured reviews

JJ

5.0Reviewed Jul 22, 2023

Labs in this course are very helpful and to the point. It took me a while to complete this course but i learned a lot.

YC

4.0Reviewed Jan 16, 2022

Love the labs, but do not like the robotic lectures.

MA

5.0Reviewed Jun 9, 2022

Thanks to all the instructor's efforts, one of the best DATA engineering courses, contains hands-on Experience with essential data tools.

SG

5.0Reviewed Jul 12, 2023

Learn a lot about Apache Airflow, Kafka from sketch.

OH

4.0Reviewed Jan 25, 2022

It's great introduction for airflow and kafka but still an introduction it is shallow doesn't offer much but at the end you will understand what you need to continue further in both technologies.

DR

4.0Reviewed Jun 3, 2022

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

PM

4.0Reviewed Mar 23, 2023

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

KB

5.0Reviewed Apr 23, 2022

Nice intro to ETL and Data Pipelines. Beginner level easy to follow hands on Airflow and Kafka.

HT

4.0Reviewed Mar 31, 2023

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

RS

5.0Reviewed Mar 13, 2022

Succinctly presented. Labs really hammered the point home :)

BN

5.0Reviewed 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.

DL

5.0Reviewed Sep 6, 2022

V​ery useful high-level overview with practical examples of the major technologies that drive modern data pipelines.

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