Transforming raw data into enriched meaningful data

Every data analytics system consists of a few key stages, including data ingestion, data transformation, and loading into a data warehouse or a data lake. Only after the data passes through these stages does it become ready for consumption by end users for descriptive and predictive analytics. There are two common industry practices for undertaking this process, widely known as Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT). In this section, you will explore both these methods of data processing and understand their key differences. You will also learn about the key advantages ELT has to offer over ETL in the context of big data analytics in the cloud.

Extracting, transforming, and loading data

This is the typical data processing methodology that's followed by almost all data warehousing systems. In this methodology, data is extracted from the source systems and stored in a temporary storage location...