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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About this Course
Computer and IT literacy.
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Try Coursera for BusinessWhat you will learn
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.
Skills you will gain
- Extraction, Transformation And Loading (ETL)
- Apache Kafka
- Apache Airflow
- Data Pipelines
Computer and IT literacy.
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessOffered by
Syllabus - What you will learn from this course
Data Processing Techniques
ETL & Data Pipelines: Tools and Techniques
Building Data Pipelines using Airflow
Building Streaming Pipelines using Kafka
Reviews
- 5 stars68.75%
- 4 stars19.88%
- 3 stars5.11%
- 2 stars3.97%
- 1 star2.27%
TOP REVIEWS FROM ETL AND DATA PIPELINES WITH SHELL, AIRFLOW AND KAFKA
Very useful high-level overview with practical examples of the major technologies that drive modern data pipelines.
Excellent introduction to this topics. Labs contain all you need to know how to start using this type of technologies. Highly recommended.
Thanks to all the instructor's efforts, one of the best DATA engineering courses, contains hands-on Experience with essential data tools.
This was my first introduction to Apache airflow and i found the course detailed and practical.
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