2.2 ETL vs. ELT and REST APIs

2.2.1 ETL vs. ELT

The order in which you extract, transform, and load data has significant implications for pipeline speed, flexibility, and data quality.

ETL (Extract-Transform-Load) extracts raw data from the source, transforms it in a staging area, then loads the transformed data into the target destination.

ELT (Extract-Load-Transform) loads raw data directly into a cloud data warehouse (e.g., Redshift, Snowflake), then transforms it within the warehouse. This allows flexible transformations to be applied later.

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Advantages of ELT

• Faster implementation and data availability
• More flexibility in transformations
• Suitable for semi-structured/unstructured data (e.g., JSON, text, images)

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Downsides of ELT

The main risk is creating an “EL pipeline” where no transformation ever happens, turning your data warehouse into a data swamp.

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Comparison of ETL vs. ELT

Feature	ETL	ELT
History	Developed in the 80s/90s when storage was expensive	Gained popularity in the cloud era
Transformation Timing	Before loading	After loading
Load Time	Longer	Faster
Flexibility	Structured data only	Structured, semi-structured, and unstructured data
Scalability	Manual effort required for scaling	Uses cloud warehouse power for large-scale processing
Data Quality	Ensures data quality before loading	Requires transformations after loading

2.2.2 REST API

APIs are a fundamental ingestion mechanism. Jeff Bezos famously enforced API-based communication within Amazon, a mandate that laid the foundation for AWS.

What is an API? A set of rules and specifications for programmatic communication between applications, typically including metadata, documentation, authentication, and error handling.

REST API (Representational State Transfer) is the most common API type, using HTTP as its communication protocol.

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HTTP Request Types

Method	Purpose
GET	Retrieve a resource
POST	Create a resource
PUT	Update/replace a resource
DELETE	Remove a resource