ETL Vs ELT: Key Differences, Advantages and What to Choose?
In data management, the choice between Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) has become a critical consideration for businesses striving to optimise their data transformation strategies. The fundamental distinction lies in the processing sequence, ETL transforms data before loading it into a data warehouse, while ELT shifts the transformation process to the data warehouse after the data has been loaded. Understanding these differences is crucial in aligning data processes with business objectives. Both approaches play a vital role in data integration but follow different methodologies. The choice between ETL and ELT extends beyond their processing sequence. Factors such as data privacy, compliance, infrastructure cost and processing speed significantly influence the selection of the appropriate approach. This blog explains the difference between the ETL and ELT processes, their advantages and compatibility in specific environments. It will also help you understand which one would best serve your organisation’s data needs.
What is ETL?
ETL: Extract, Transform, Load
Extract: During the extraction phase, raw data is gathered from multiple sources like databases, files, spreadsheets, and SaaS applications.
Transform: During the transformation phase, the extracted data is cleansed, formatted, and standardised in a staging area.
Load: During the loading phase, the transformed data is transferred to a data lake or data warehouse for storage and analysis.
ETL and Data Warehouses
OLAP (Online Analytical Processing) data warehouses require data in a relational format, making ETL essential for transforming and mapping data before loading to ensure accurate analysis.
Structured Data Flow
ETL follows a well-defined process of extracting data from various homogeneous or heterogeneous sources, transferring it to a staging area for cleansing, enrichment and transformation, finally loading it into a data warehouse for analysis. This ensures data is refined and ready for business insights.
Traditional Challenges
Legacy ETL processes often require significant effort, involving extensive planning, manual coding, and continuous oversight by data engineers. Updating workflows and integrating new data sources can be time-consuming and resource-intensive.
Modern ETL Solutions
The emergence of cloud-based ETL tools has streamlined the process, reducing the need for manual development and accelerating data integration. These platforms enable faster, more efficient pipeline management while simplifying transformation logic and enhancing collaboration.
Scalability and Flexibility
With modern ETL solutions, organisations can quickly integrate data from multiple sources without heavy infrastructure dependencies. This ensures a scalable and flexible data processing approach, especially for cloud based data warehouses.
Key Advantages of ETL
ELT: Extract, Load, Transform
What is ELT?
ELT is a data integration process where data is first loaded into a storage solution before being transformed. Unlike traditional ETL, ELT eliminates the need for a staging area, allowing structured, semi-structured, and unstructured data to be directly loaded into cloud-based storage, such as Azure Data Lake. The transformation is then executed within the compute layer of modern data warehouses like Databricks or Microsoft Synapse, which are designed to separate compute from storage. This architecture enables scalable, high-performance processing while maintaining flexibility in data management.
Data is stored in the lake, but the transformation is performed in Databricks. This separation allows for optimised resource utilisation, ensuring efficient data processing without compromising storage management.
ELT: Extract, Load, Transform
Extract: Raw data is extracted from multiple sources, similar to the ETL process.
Load: Raw data is directly loaded into a data store without prior cleaning or standardisation, unlike ETL.
Transform: Data transformation occurs last, where it is cleaned and standardised within the data warehouse instead of a staging area.
ELT and Data lakes
The ELT process is closely aligned with data lakes, enabling seamless storage and processing of vast amounts of structured and unstructured data. Unlike traditional OLAP data warehouses, data lakes allow raw data to be directly loaded without prior transformation. The data transformation, cleansing and enrichment processes occurs before the data is prepared for analysis.
Here are key aspects to understanding about the relationship between ELT and data lakes:
Powered by Cloud-Based Infrastructure
ELT relies heavily on modern cloud-based servers with scalable storage and high-speed processing. Platforms like Databricks, Azure Synapse and Microsoft Fabric Data Warehouse make ELT possible due to their exceptional data processing capabilities, eliminating the need for traditional staging areas.
Unrestricted Data Ingestion
With ELT and data lakes, organisations can ingest vast volumes of raw data , regardless of format or structure. This flexibility allows businesses to store and access continuously incoming data without requiring upfront transformations.
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On-Demand Data Transformation
ELT only transforms data when needed for specific analysis, providing the flexibility to customise transformations according to the desired outcome. This enables businesses to generate diverse metrics, forecasts, and reports without modifying the entire data pipeline, as is required in ETL.
Limited Use Cases Compared to ETL
ELT is powerful and scalable, but it’s not always the best fit for every situation. Since transformations happen after loading, it can sometimes slow down analysis if not optimised well. Also, ELT tools are still evolving, so for very complex data needs, some organisations may find traditional ETL more reliable. That said, with the right setup, ELT can be a great choice for modern data processing.
ELT combined with data lakes offers unmatched scalability and flexibility in handling raw data, but it may require specialised skills and infrastructure to fully leverage its potential.
Key Advantages of ELT
ELT’s ability to directly load raw data without pre-transformation makes it an ideal choice for businesses prioritising speed, flexibility, and scalability.
ETL and ELT Cost: Factors to Consider
Below are some major cost factors to be considered:
· Infrastructure and Hardware Costs
· Processing Costs and Scalability
· Labor and Implementation Expenses
· Long-Term Maintenance Considerations
· Industry-Specific Cost Considerations
· Cost Optimisation
ELT or ELT: What to Choose?
Choosing between ETL and ELT depends on various factors, here’s a breakdown:
Data Volume
If you deal with large volumes of data including huge unstructured or semi-structured data, ELT would be the best choice. ELT is a more suitable solution due to its scalability and ability to handle diverse data types efficiently for large volumes of data.
Smaller volumes of structured data can be processed using ETL where data quality is more important than scalability.
Processing Capabilities
Processing capabilities play a vital role in selecting the right method, however, this highly depends on the target system. For example, a data warehouse with robust processing capabilities, ELT is for you.
Whereas a target system with inefficient processing power, ETL may be required to perform transformations on a separate server.
Data Quality and Compliance
Where data governance and quality standards are crucial, ETL is the most suitable solution. Quality checks and data cleansing are performed before the loading takes place. While ELT is suitable for industries where data quality and governance can be conducted after loading, which can be resource intensive.
The decision ultimately hinges on the specific data architecture, regulatory requirements, business goals and processing capabilities.
Conclusion
Choosing between ETL and ELT depends on your organisation’s data structure, processing needs, and infrastructure. ETL ensures high data quality and compliance, making it ideal for structured data, while ELT offers speed, scalability, and flexibility, which is best suited for large, unstructured datasets.
Many modern organisations use a hybrid approach, combining ETL for legacy systems and ELT for cloud-native architectures.
Understanding these differences will help you align your data strategy with your business goals, ensuring optimal performance and efficiency.
At Cloudaeon, we specialise in optimising ETL processes across platforms like Databricks , Microsoft Azure Synapse and Microsoft Fabric to ensure that your data pipelines run smoothly, efficiently and cost-effectively.
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