ELT (Extract, Load, Transform) and ETL (Extract, Transform, Load) are two common approaches for moving data from source systems into a target database or data warehouse for analysis and reporting. While they share some similarities, there are important differences between ELT and ETL processes.
ELT Overview
ELT stands for Extract, Load and Transform. It is a data integration process that works by:
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Extracting data from one or more sources such as databases, APIs, files, etc.
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Loading the extracted raw data directly into the target system, which is usually a data warehouse or data lake.
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Transforming the data inside the target system into the desired format for analysis and reporting.
The key difference from ETL is that ELT transforms the data after loading it into the target destination. This approach provides faster data availability since no separate transformation step is required before loading.
How ETL Works
In contrast to ELT, ETL stands for Extract, Transform, Load. The key steps are:
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Data extraction from sources.
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Transforming the extracted data by cleaning, validating, enriching, formatting, etc. This transformation is done on a separate server or system.
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Loading the transformed data into the target database, data warehouse or other system.
In ETL, the major difference is that all transformations happen before the data is loaded into the final target. This approach takes more time due to the additional transformation stage.
Key Differences Between ELT and ETL
Some of the main differences between ELT and ETL processes include:
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When Data Transformation Happens – ELT transforms after loading, ETL transforms before loading.
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Speed – ELT is faster since it skips a separate transformation stage. ETL takes longer due to the added stage.
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Scalability for Large Data Volumes – ELT more easily scales to process large volumes of data. ETL can struggle with huge datasets.
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Flexibility – ELT allows re-transforming loaded data easily. ETL requires re-extracting and reloading if transformations change.
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Structured vs Unstructured Data – ELT supports unstructured data like text logs, audio, video, etc. ETL works better for structured data.
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Support for Data Lakes – ELT can load into data lakes, which store raw structured, unstructured and semi-structured data.
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Security Considerations – ELT requires more security controls since it loads raw data. ETL secures data during transformation.
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Costs – ELT tends to have lower costs by leveraging the scale of cloud data warehouses. ETL requires more hardware and maintenance.
When Should You Use ELT?
Here are some good use cases where ELT is likely the better data integration approach compared to ETL:
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When near real-time access to source data is critical for business operations. ELT provides faster data availability.
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For very high data volumes that can scale quickly. ELT is great for "big data".
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When the business needs raw data for tasks like machine learning, analytics and visualizations. ELT provides direct access to raw data.
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For combining diverse structured, unstructured, and semi-structured data in a data lake.
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To support iterative analytics on data that may require transformations like pivots, aggregates, etc. ELT allows quick re-transformation.
Challenges in Moving from ETL to ELT
For organizations already using ETL processes, moving to ELT can present some challenges:
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Code/Logic Changes – The extract and load code will need to be updated since transformations now happen after loading. Additional code may be needed for post-load transformation steps.
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Increased Data Security Needs – With ELT, stricter data security controls will be required for the raw data loaded into the target system. Data masking, encryption, or access controls need to be added.
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Schema/Model Changes – Database schemas and data models may need adjustments to support storing raw data for transformation after loading.
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Different Skillsets Required – Data engineers accustomed to ETL processes will need new skills for ELT like running transformations on raw data at scale.
Top ELT Tools
Some popular ELT tools to consider include:
- Informatica Cloud Data Integration
- Oracle Data Integrator and Data Transforms
- Azure Data Factory
- Amazon EMR
- Palantir Foundry
- Fivetran
- Matillion ETL
- Hevo
- Xplenty
When evaluating ELT tools, key selection criteria include ability to connect to all data sources, ease of use, scalability to handle large data volumes, and robust security capabilities. The ideal solution should be able to extract data from diverse sources, load it quickly at scale into data warehouses or lakes, and provide an interface for easily transforming the loaded data on demand.
Conclusion
In summary, ELT and ETL offer two different approaches to managing the data integration process, with ELT providing faster data availability but requiring more advanced data security. As data volumes continue growing, ELT‘s flexibility and scalability makes it a popular choice for modern data analytics pipelines. However, moving from ETL to ELT comes with technology and process changes that need to be managed. With the right strategies and tools, organizations can take advantage of ELT architecture to empower faster business insights.
