Extract, Load and Transform (ELT)

What is ELT?

ELT (Extract, Load, Transform) is a data processing approach where raw data is extracted from various sources, loaded directly into a target system, such as a cloud data warehouse, and then transformed within that system. It differs from ETL (Extract, Transform, Load) by performing the transformation step after the data is stored. This method takes advantage of the high storage and computational power of modern platforms, making it suitable for large-scale data processing.

How ELT Works

• Extract: The process begins by gathering raw data from different sources like databases, APIs, or files. This step ensures all relevant data is collected without modification.
• Load: The raw data is then loaded into the target system. Modern cloud platforms, such as Snowflake or Google BigQuery, are commonly used because of their scalability and ability to handle large datasets.
• Transform: Finally, the data is processed within the storage system. This step involves cleaning, organizing, and structuring the data to make it ready for analysis or reporting.

Advantages of ELT

• Faster Data Loading: ELT skips the transformation step during extraction, allowing data to be loaded more quickly.
• Leverages Modern Platforms: It uses the computational power of cloud-based systems, reducing reliance on external tools.
• Scalable for Big Data: ELT efficiently handles large datasets, making it ideal for businesses with massive data needs.
• Supports Real-Time Analysis: The quick loading process enables faster insights and decision-making.
• Simplified Architecture: ELT eliminates the need for intermediate storage, streamlining the process.

Disadvantages of ELT

• Requires Advanced Systems: ELT depends on robust and often expensive cloud platforms to manage and process data.
• Complex Data Management: Transforming raw data after loading can be challenging, especially with complex datasets.
• Data Security Concerns: Loading unprocessed raw data into storage requires strict security protocols to ensure compliance and protect sensitive information.
• Limited for On-Premise Systems: ELT is primarily designed for cloud-based environments, making it less suitable for traditional setups.

Aspect	ETL (Extract, Transform, Load)	ELT (Extract, Load, Transform)
Processing Order	Transform data before loading into storage.	Load raw data first, then transform it in storage.
Data Transformation	Happens outside the target system.	Happens within the target system.
Performance	Depends on the ETL tool’s processing power.	Leverages the target system’s computational power, often faster.
Data Volume	Suitable for smaller to medium datasets.	Ideal for handling large datasets and big data.
Target System	Works well with traditional on-premise databases.	Best suited for modern cloud-based platforms.
Flexibility	Limited flexibility after transformation.	Greater flexibility to adapt and reprocess data after loading.
Real-Time Processing	Less suitable for real-time needs.	Better for real-time or near-real-time analytics.
Implementation Cost	Lower initial cost for simpler systems.	Higher initial cost due to reliance on advanced platforms.
Complexity	Easier to set up and manage.	Can be more complex, especially for data transformation.
Use Cases	Historical data analysis, small systems.	Big data analytics, real-time monitoring, and cloud integrations.
Examples	Traditional data warehouses.	Cloud platforms like Snowflake, BigQuery, and Redshift.