Introduction
Compare transformation, scalability, speed, cost, privacy & use cases. ETL for small/complex data; ELT for big data/cloud. Which fits your pipeline?
What is ETL (Extract, Transform, Load)?
ETL is a traditional data integration process that gathers data from various sources, transforms it through staging tables or predefined steps, and then loads it into a data warehouse or repository. This method emphasizes the transformation of data before it reaches its final destination, allowing for the cleansing, aggregation, and organization of data to fit specific analytical needs. Originating in the 1970s, ETL has become synonymous with data integration, offering a structured approach where data is prepared for analysis before being stored, ensuring that analysts have access to ready-to-use, high-quality data for their reports and dashboards.
What is ELT (Extract, Load, Transform)?
The only difference in ELT compared to ETL is where the transformation takes place. In ELT, the transformation occurs in the destination data storage. ELT leverages the processing power of the destination storage for data transformation. This simplifies the architecture by removing the transformation engine from the source to destination path. The major advantage of ELT is the enhancement in the capacity of the destination store and directly improves the performance of the ELT process. However, the effectiveness of ELT depends on the destination system’s ability to handle data transformations efficiently. Cloud data warehouses such as Snowflake, Amazon Redshift, Google BigQuery, and Microsoft Azure all have the digital infrastructure, in terms of storage and processing power, to facilitate raw data repositories and in-app transformations.
Why is ELT the future of data?
1. Efficiency and Speed: In ELT, data is transformed only after being loaded into the destination system. ELT significantly increases the ingestions of data to the destination as there is no transformation stage in between. Analysts can also perform transformations within the data warehouse environment without needing to rely on data engineers.
2. Raw Data: ELT stores raw data directly in the destination system unlike ETL, offering an auditable source of truth and eliminating the need to reload or re-source data to support new use cases. This approach ensures data integrity and speeds up the process to support new formats of data, as the original raw data is readily accessible for re-transformation if needed.
3. Large Volume: ETL is suitable for small data sets that require very complex transformations. But ELT is ideal for larger data sets with more emphasis on getting real-time data for analysis.
ETL vs ELT: Comparison
| ETL | ELT | |
| Transformation | Data is transformed prior to being loaded into the destination system. | Data is transformed post-loading into the destination. |
| Volume | ETL takes longer to load data into the destination due to pre-loading transformations. | ELT is faster as data is directly loaded into the destination. |
| Scalability | Requires anticipating use cases and defining transformation logic upfront. Changes to data models or new use cases often require reworking the pipelines. | Enables greater flexibility by sorting raw data first and applying transformations as needed. New use cases and models can be developed without rebuilding ingestion pipelines. |
| Speed | Slower overall processing since data must be transformed before loading, adding latency to data availability. | Faster ingestion and time-to-insight, as data is loaded immediately and transformed within the destination system when needed. |
| Cost | Higher upfront and operational costs due to separate transformation infrastructure and ongoing maintenance. | Most cost-effective by reducing the need for specialized ETL infrastructure and maximizing existing cloud warehouse compute resources. |
| Privacy | Data can be filtered, masked, or anonymized before loading, making it well-suited for environments with strict compliance requirements. | Raw data is stored in the destination system, requiring strong governance, access controls, and security policies to ensure sensitive data is protected. |
| Maintenence | Requires ongoing maintenance as pipelines are tightly coupled to predefined schemas and transformations. | Easier to maintain since transformations are modular, versionable, and executed within the data warehouse environment. |
| Load Time | Better suited for smaller data sets needing complex transformations. | Suitable for large datasets, focusing on real-time data analysis. |
| Flexibility | Anticipate use cases and design data models in advance, or be prepared to completely overhaul the data pipeline. | Develop new use cases and design data models as needed. |
| Use Cases/Best For | Best for smaller datasets, legacy systems, and scenarios requiring complex transformations or strict data validation before storage. | Ideal for large-scale analytics, cloud-native architectures, real-time reporting, machine learning workflows, and evolving data needs. |
Which one to choose for your needs?
Choosing between ETL and ELT comes down to how your organization uses data and how quickly it needs to act on it.
Choose ETL if you need to work with smaller datasets that require complex transformations before storage. Organizations in highly regulated industries often prefer ETL because it allows for data masking, filtering, and anonymization during the transformation phase, ensuring compliance requirements are met before data enters the destination system. ETL also works well for legacy systems and on premise infrastructure where the destination warehouse has limited processing capabilities.
Choose ELT if you handle large volumes of data and prioritize speed and flexibility. ELT is designed for modern cloud data warehouses like Snowflake, BigQuery, and Redshift, which can transform data efficiently after loading. By storing raw data in its original form, you maintain an auditable source of truth and can create new transformations without re-ingesting data from source systems. This flexibility supports agile analytics, machine learning workflows, and real time reporting requirements while proving more cost effective at scale.
Ultimately, the choice depends on your infrastructure, team capabilities, and growth plans. Cloud native organizations typically benefit from ELT’s scalability, while those with established systems and strict compliance needs may prefer ETL’s controlled approach.
Building and maintaining data pipelines shouldn’t slow your team down. AI Squared automates the entire data movement process, from extraction to transformation, so you can focus on what matters: turning data into insights.
