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.
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Об этом курсе
Computer and IT literacy.
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Describe and contrast Extract, Transform, Load (ETL) processes and Extract, Load, Transform (ELT) processes.
Explain batch vs concurrent modes of execution.
Implement an ETL pipelinethrough shell scripting.
Describe data pipeline components, processes, tools, and technologies.
Приобретаемые навыки
- Extraction, Transformation And Loading (ETL)
- Apache Kafka
- Apache Airflow
- Data Pipelines
Computer and IT literacy.
Будет ли вашей компании выгодно обучить сотрудников востребованным навыкам?
Попробуйте Coursera для бизнесаот партнера
Программа курса: что вы изучите
Data Processing Techniques
ETL & Data Pipelines: Tools and Techniques
Building Data Pipelines using Airflow
Building Streaming Pipelines using Kafka
Рецензии
- 5 stars65,78 %
- 4 stars21,71 %
- 3 stars5,26 %
- 2 stars4,60 %
- 1 star2,63 %
Лучшие отзывы о курсе ETL AND DATA PIPELINES WITH SHELL, AIRFLOW AND KAFKA
Great Course, Assignments prepare really well and flexible
It takes 1 hour to connect the lab and start the service.
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.
Very useful high-level overview with practical examples of the major technologies that drive modern data pipelines.
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