ETL vs. ELT—Key Differences, Improvements, and Trends
Traditionally, data integration and analytics were accomplished through ETL, which stands for Extract, Transform, and Load. This process involved extracting data from various sources, transforming it, and loading it into a data warehouse.
Transforming data before loading became less relevant as data warehouses emerged with strong querying layers. Querying engines also evolved and allowed operations on raw data in a data lake. It became easier to load the data into a data lake or warehouse and transform it later according to the downstream system’s requirements. This paradigm is called Extract, Load, and Transform (ELT).
This article explains the concepts behind ETL and ELT, the differences, and how Gen AI changes the way for both.
Summary of key ETL & ELT concepts
| ETL | ELT | |
|---|---|---|
| Order of execution | ETL uses data transformation engines to manipulate data before loading it into the final destination. | ELT operates by loading data in its raw or intermediate format to the target destination and then transforming it using the target system’s processing power. |
| Transformation | A separate transformation engine transforms data before it reaches the warehouse. Downstream systems that use the data have little control over it. | The data warehouse’s querying engine transforms data after loading it. Downstream systems can manipulate and create feeds according to their requirements. |
| Data governance | ETL makes enforcing governance policies and PII filtering easier during the transformation stage. The downstream teams only get access to filtered data, adhering to all governance and privacy considerations. | Governance and privacy considerations must be configured separately in the data warehouse layer using row-based access control and PII column filtering techniques. |
| Destination | In the case of ETL, the destination is usually a well-structured data warehouse. An intermediate staging area may be a data lake or block storage. | ELT is very flexible in terms of the destination. It can be a data lake, a data warehouse, or a modern lakehouse. Any destination with a querying layer or transformations based on support from the schema-on-read approach is suitable for ELT. |
Understanding ETL and ELT
The objective of ELT and ETL is to integrate data from various sources and make it ready for reporting and analytics. Traditionally, the organization’s transactional database for day-to-day operations was the primary data source. In the modern data landscape, data sources handled by ELT and ETL systems are endless, and every action is a data point. It could be an event stream of customer actions or data feeds from third-party providers like vendors or domain data specialists.
Understanding ETL
ETL is the traditional data integration process that creates a consistent data set for downstream analytics and machine learning. It involves cleaning and organizing data from various sources into a suitable form for generating multiple reports.
The destination system for an ETL pipeline is usually a data warehouse or a data lake. The ETL process assumes that the format and structure of the output data are known beforehand and that downstream systems use them for use cases like report generation, model building, etc. Usually, no complex data transformations are expected after the ETL process finishes.
ETL data flow
A typical organization has several data sources that must be extracted, transformed, and loaded for downstream use cases. At times, there may be a staging area to aid raw data storage and data exploration before the transformation process. Organizations can enforce data privacy and security policies at the transformation stage—separating the eventual users from anything they need not know. After transformation, little to no change is expected for the downstream systems to utilize the data.
Transformations in an ETL pipeline are executed by separate data processing engines like Spark or Hadoop or cloud-based managed services based on these. Such transformation engines are independent of the source and destination databases.
As the industry evolved and the technology behind big data processing improved, the assumption about predefined output data format and structure became a bottleneck. Analytics use cases in a typical organization started increasing, and the lack of flexibility related to the transformation part of ETL became more evident, leading to the evolution of ELT.
Understanding ELT
ELT moves the transformation process to the end of the sequence compared to the traditional ETL process. It aims to create data products that multiple downstream systems can customize and use according to their use cases. ELT works based on the assumption that downstream systems write transformation logic according to their requirements.
ELT data flow
Two critical advancements in data processing technology made transformation close to the usage point possible. The first was the emergence of strong querying layers in the data warehouse. The second was the advent of querying engines that operate on the ‘schema-on-read’ paradigm. Such querying engines could operate directly on the data lake and transform data on the fly whenever required.
A key advantage of ELT over ETL is that data is available soon. While this provides greater flexibility to the end users, it creates issues enforcing data security and role-based access control. Such rules are generally implemented during the transformation stage, and the transformation delays enforcement till the point of usage, leading to complexities in access control implementation.
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ETL vs. ELT—key differences
Now that we understand the fundamental differences between ETL and ELT let us explore them in detail.
Order of execution
The primary difference between the two architectural patterns is whether or not the transformation step executes before loading. Moving the transformation execution point of ELT to the end is possible because of the strong querying layers of the destination systems. In the older days, transformation engines were separate from the data warehouse, and hence, it was easier to execute data before loading it to the destination.
End user flexibility
ETL assumes that end-user requirements are known beforehand and will not change much during the job’s lifetime. Naturally, this enforces constraints on the use cases that can be implemented. Conversely, ELT puts transformations closer to the usage point of data products and even enables downstream systems to transform data as per their requirements.
Nature of transformation engine
In the case of ETL, the transformation engine is separate from the source and destination systems. A typical example is using an ETL tool like Informatica with transformation support or a transformation engine like Spark before loading data to the data warehouse.
In contrast, ELT uses the destination system’s data processing capability or a separate transformation layer based on the schema-on-read paradigm. An example of this is using an MPP database like Redshift or Snowflake as the destination system and using its query layer for transformation.
Enforcing data governance and security
Restricting access to data to only those who need it is very critical in ensuring compliance with frameworks like GDPR, HIPAA, etc. You can do this best by handling privacy-related manipulations like masking, access control restrictions, etc, in the transformation layer. Since the transformation happens before loading data in ETL, the end users are sufficiently separated from the original data. In the case of ELT, enforcing restrictions poses more challenges than ETL.
Data warehouses, lakes, and lake houses
Traditionally, ETL relied on a structured data warehouse where the final data is to be loaded. With the advent of data lakes and lake houses, there is no need for the destination systems to follow a rigid structure.
A data lake differs from a data warehouse because it can store data in raw format without a predefined structure. A lakehouse combines the abilities of a data warehouse and data lake by bringing a metadata layer, querying engine, and ACID transactions over the lake.
While nothing prevents one from implementing an ETL on top of a data lake, it is counterintuitive. The very purpose of a data lake and lake houses is to store structured and unstructured data and expose them for transformations through querying layers.
ELT was envisioned in a world where data lakes and lake houses already existed. Hence, ELT is more suitable for use cases that involve the two.
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Factors to consider while choosing between ETL and ELT
Choosing between ETL and ELT for a use case is not an easy task. Both have their pros and cons. The below section attempts to provide pointers that can help make this decision.
Business requirements
The choice between ETL and ELT is often dictated by business requirements related to cost and time of data availability.
ELT prioritizes making data available as soon as possible and enables flexible development of new use cases. Unfortunately, this flexibility comes at increased computing costs. Since downstream teams control transformations, it is challenging to determine compute expenses in advance.
Transformation requirements
Transformations in an ELT system rely on the querying ability of the destination data warehouse or lake. While it is sufficient for most use cases, there are times when a complex transformation requires very high computing power and highly optimized logic implementation. In such cases, ETL is preferred over ELT.
Cost considerations
Some data lakes/warehouses bill for transformations as compute costs. This can make ELT expensive. ETL SaaS offerings like Nexla usually don’t bill for computing costs. Such platforms make it cost-effective to implement both ETL and ELT, even with complex transformation requirements.
Analytics requirements
ELT fits well in cases where analytics requirements are unknown beforehand and development teams must implement new reports within short notice. In such cases, having control over the transformation is ideal.
Regulatory requirements
In ELT, organizations are limited to configurations provided at the data warehouse or data lake layer to implement regulatory and governance policies. ETL provides a much more robust mechanism for implementing policies and separating original data from downstream users.
Storage requirements
All historical data is available in ELT for the downstream teams to query and investigate. In the case of ETL, downstream teams only get access to aggregated data specific to their use cases.
What is the impact of GenAI on Data Engineering?
How Gen AI is creating a shift in data integration approaches
Gen AI is bringing drastic changes to the data engineering space. It reduces dependencies on model training since everything is available as APIs or directly inferable models. Building prototypes is now easier than ever. However, taking these models to production is another story. Data engineering has become more critical because of the complexities involved.
While building prototype applications using pre-trained models is quick, taking them to production requires one to try out and evaluate several models. This involves complex tasks like preparing test sets, maintaining model and prompt versions, and orchestrating multi-model inference pipelines that increase data engineering complexity.
Vector pipelines that convert organizational data to vectors pose another challenge. Data must be chunked optimally with adequate metadata for RAG pipelines to work effectively. Complex data transformations are necessary to ensure the response quality and preside over misleading responses.
The requirements from a data engineering platform have evolved considerably with the advent of Gen AI. This is where a fourth-generation integration platform becomes relevant.
How a data management platform can help in ETL and ELT
Be it ETL or ELT, the core of the problem is keeping up with endless transformation requirements to facilitate analytics. In reality, the architectures followed by organizations may not belong to one among ELT or ETL. Organizations follow a hybrid approach, EtLT, where transformations happen before and after loading data based on requirements.
In such a landscape, one must prioritize enabling developers to focus on the core business transformations. This is where a good data management platform like Nexla can help.
Nexla is a fourth-generation integration platform invalidating terms like ETL, ELT, or other architectural jargon. Development teams can focus on core business transformation requirements. Nexla helps organizations abstract data engineering requirements into data products called NexSets. Data products combine readily available reusable data with metadata like schema, lineage, characteristics, and sample data. NexSets extend beyond constraints imposed by architectures like ETL and ELT to bring much-required flexibility in the modern AI-enabled world.
Most data platforms charge for computing and storage separately. Nexla is an exception here and does not charge for computing.
On a high level, Nexla provides the following capabilities:
Comprehensive connector support for data extraction and load
Be it ETL or ELT, integrating with several data sources and creating a unified view is the objective of a data integration system. A data integration system also has to integrate with various target systems. A platform with built-in connectors to interact with various data sources and targets can reduce development time and time to production.
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No code or low code approach for transformations
In typical organizations, people with fundamental data knowledge, like business analysts and domain experts, are not programmers. A no-code or low-code platform that generates transformations from visual representations can bring people without programming knowledge to the center of ETL or ELT implementation. Nexla’s NextSet Designer helps developers create data products through transformation rules. The AI-powered Designer recommends transformation rules based on the selected data attributes—reducing development time.
Data lineage tracking
Keeping track of the origin of various data products and auto-generating metadata for them goes a long way in implementing self-service analytics where the end users can make sense of data independently.
Governance policy enforcement
A good data management platform also makes it easy to enforce governance and data security rules. They come with predefined jobs for implementing data masking and role-based access. Such platforms can abstract out implementing governance and security policies through simple configurations.
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Conclusion
An organization must expand beyond a single architecture in the modern data-driven world. It has to work with several kinds of architectures and their combinations to get the best results. With the advent of Gen AI, this has become all the more important because of the need to move data from diverse systems to AI model input feeds. A platform like Nexla makes this possible irrespective of your existing data infrastructure.