ELT

1. Overview

Definition: ELT is a data processing paradigm where data is first extracted from sources and loaded into a storage system, typically a data warehouse or a data lake, before transforming it into the desired format or structure for analysis.

Extract:
- Collecting data from various sources such as databases, APIs, and logs.
- Data is often in raw form and may not be immediately usable for analysis.
Load:
- Transferred data is moved into a centralized storage system.
- The data warehouse or lake can manage large volumes of unstructured and structured data.
Transform:
- Data is cleaned, formatted, and transformed as needed once it is already in the warehouse.
- Transformation can occur using SQL queries and other processing tools while data remains accessible for analysis.

In ETL, transformation happens before data is loaded into a target database.
ELT is often more suitable for handling big data due to advancements in cloud data warehousing technologies and the capability to quickly process and query data at scale.

Scalability: Better suited for big data environments.
Flexibility: Allows for diverse and evolving data requirements since data can be transformed as needed once it's loaded.
Real-time Analytics: Facilitates quicker access to raw data for timely insights.

Data Security and Compliance: Storing raw data might expose sensitive information before transformation.
Complexity in Management: Requires robust governance to manage data flow and ensure data quality.

Used widely in cloud computing environments and modern data platforms like Snowflake, Amazon Redshift, and Google BigQuery.

While ETL is traditionally used in on-premises environments, ELT takes advantage of cloud-based architectures and scalable computing power.