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.Read more.
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The techniques and tools covered in ETL and Data Pipelines with Shell, Airflow and Kafka are most similar to the requirements found in Data Engineer job advertisements.
ETL and Data Pipelines with Shell, Airflow and Kafka is a part of one structured learning path.