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

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After taking this course, you will be able to describe 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. 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 importing 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. Finally, you will complete a shareable final project that enables you to demonstrate the skills you acquired in each module....



Oct 11, 2022

Course Is Good but, if you can add some more practicles that will surely help understand better and help all learner grasp things very quickly.


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.


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