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ETL vs. ELT - A short history lesson

>_TLDR Due to plummeting storage costs, the rise of cloud computing and AI, the data industry is rapidly shifting from ETL (transform then load) to ELT (load raw then transform) for greater flexibility, cost-efficiency, and future-proofing.

The conversation around ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) is central to modern data management. As data scales in volume and complexity, choosing the optimal data integration strategy is crucial for organizations. The primary difference between these two methodologies is the timing of data transformation: ETL transforms data before loading, while ELT loads raw data first and performs transformations within the data warehouse. This article examines the industry’s increasing preference for ELT, highlighting its processes, benefits, and the underlying reasons related to cost-effectiveness and future readiness.

What is ETL?

ETL stands for Extract, Transform, Load. This method prepares data for analysis by extracting it from various sources, transforming it into a structured format, and then loading it into a target system. The ETL process:

  • Extract: Data is pulled from various sources, often in unstructured or semi-structured formats.
  • Transform: The data undergoes a transformation process, cleaning, formatting, and structuring it for analysis.
  • Load: Once transformed, the data is loaded into a target system, typically a data warehouse, where it becomes available for querying and reporting.

For a long time, ETL was the undisputed champion, largely born out of necessity. Storage, particularly cloud storage in its nascent stages, was expensive. To keep costs in check, it was imperative to limit the volume of data stored in the warehouse. This often meant carefully curating and transforming data before it ever touched the storage layer. There were clear advantages to this approach. Data models were typically well-defined and robust, as a “build it right the first time” mentality was enforced by the cost constraints. This led to highly structured and optimized data environments. However, ETL also presented significant limitations. Adding new fields or columns after the initial data ingestion was a complicated process, often requiring a complete re-extraction and re-transformation of the original data, which might have been discarded post-transformation. Furthermore, the ETL paradigm demanded a high degree of foresight: stakeholders had to know precisely what data they needed and in what format before integration, making ad-hoc requests or exploratory data analysis challenging. A simple request like “I want data from this API” wasn’t enough; it needed to be a meticulously defined schema. Most meaningful data transformation often occurred outside this primary pipeline in a downstream business intelligence (BI) platform.

What is ELT?

ELT stands for Extract, Load, Transform and has gained popularity in cloud-native platforms like Snowflake. In this approach, data is extracted and loaded into a data warehouse first, allowing the data to be transformed using the warehouse’s computing power. ELT has emerged as a paradigm for how to manage information flows in a modern data warehouse, representing a fundamental shift from how data previously was handled. The ELT process:

  • Extract: Data is collected from various sources, just as in ETL.
  • Load: The raw data is loaded into a data warehouse without any transformations.
  • Transform: After the data is stored, transformations are performed within the warehouse, leveraging its computational power.

Enter cheaper storage, particularly in the cloud, and the landscape began to transform. The plummeting cost of storing vast amounts of data opened the door for ELT. In an ELT pipeline, you extract the raw data from source and dump it into your data lake/data warehouse. The transformation step is then performed after the data has been loaded into the destination. Transitioning from ETL to ELT means that you no longer have to capture your transformations during the initial loading of the data into your data warehouse. Rather, you are able to load all of your data, then build transformations on top of it.

Why ELT is the Modern Data Standard

ELT aligns with the scalability and flexibility of modern data stacks, enabling organizations to work with large datasets more efficiently. While there are many benefits to using ELT over ETL, below are the primary advantages driving its adoption:

  • Leverage Cloud Infrastructure: ELT takes advantage of the massive processing power of cloud-native data platforms like Snowflake, Google BigQuery, and Amazon Redshift. By loading raw data into the warehouse first, ELT enables these systems to handle transformations at scale, which is particularly valuable when working with large volumes of data.
  • Faster Data Availability: With ELT, raw data is loaded into the warehouse immediately, making it accessible for analysis more quickly. This reduces the delay often seen in ETL processes, where data must be transformed before it’s available for querying.
  • Cost Efficiency: ELT reduces the need for expensive on-premises hardware or complex ETL tools. Instead, it capitalizes on the inherent processing capabilities of cloud data warehouses, optimizing both performance and cost. In modern data stacks, offloading transformation tasks to cloud services can lead to significant cost savings.
  • Flexible, Iterative Transformation: ELT allows for more flexible data transformations. Since the raw data is already in the warehouse, analysts and data engineers can transform data iteratively, applying changes and optimizations without having to reload or reprocess the entire dataset. This flexibility makes it easier to adapt to evolving business needs and ensures that teams can always work with the latest data.
  • Data Democratization: By loading raw data into the data platform or warehouse first, ELT supports a more self-service data model. Analysts and data teams can access and transform data as needed without being bottlenecked by upstream ETL processes. This democratization fosters greater agility and collaboration across teams.

How AI Propels the ELT Shift

AI’s emergence has significantly accelerated the shift to ELT, driven by its inherent need for raw, untransformed data to discover complex patterns. These signals are often lost in ETL transformations. Modern data platforms allow for rapid loading of raw data, followed by advanced engineering and data analysis using scalable compute and AI capabilities. Furthermore, AI itself is being embedded into ELT pipelines, offering intelligent automation for schema mapping, data cleaning and other processing, while also enabling the analysis of unstructured data.

Embracing ELT: Efficiency and Agility for the Age of AI

The journey from ETL to ELT is a compelling story of technological evolution driven by economic shifts and the increasing demands of modern data analytics. While ETL, born from the constraints of expensive storage, produced well-defined but rigid data models, the advent of affordable cloud storage paved the way for ELT’s flexibility and scalability. This fundamental shift from transforming data before loading to loading raw data first and transforming it in a data platform or warehouse not only offers significant cost efficiencies and faster data availability but also empowers iterative development and greater data democratization. Crucially, the rise of Artificial Intelligence has further propelled ELT’s adoption. As data volumes continue to explode and analytical needs become more sophisticated, ELT stands as the more future-proof and agile approach, enabling organizations to harness their data’s full potential in an increasingly AI-driven world.