ETL vs ELT: Explore the key differences, trace their evolution, and learn specific use cases to determine which one is best for you.
Article Updated November 15, 2024
Data now anchors the core of numerous businesses, making data integration one of the main challenges to solve. For data engineers, much of their work revolves around transporting data via pipelines, often defined as either ETL or ELT. While ETL has stood as the standard for pipeline management for years, the rise of cloud technology tilts the scales towards ELT.
This article dives into the difference between ETL and ELT, tracing their evolution and pinpointing their distinct applications, helping you discern which fits your needs best.
In data integration, ETL and ELT are both pivotal methods for transferring data from one location to another.
ETL (Extract, Transform, Load) is a time-tested methodology where data is transformed using a separate processing server before being moved to the data warehouse.
Contrarily, ELT (Extract, Load, Transform) is a more recent approach where raw data is directly transferred to the data warehouse, and transformations occur within its confines, bypassing the need for intermediary staging processes.
Therefore, the primary difference between ETL and ELT hinges on the location of data transformation. In the ETL process, data is extracted from its source and then transformed externally on a separate processing server before it's loaded into the data warehouse. This method ensures that only processed and refined data enters the data warehouse, leaving raw data outside its confines.
On the other hand, ELT takes a slightly different route. With ELT, data is first extracted from its origin and loaded directly into the data warehouse in its raw form. It's only after this loading step that the data undergoes transformation, all of which occurs inside the data warehouse itself. As a result, ELT retains both the raw and transformed versions of data within the warehouse, providing a richer historical context that can be invaluable for certain business intelligence applications.
From this foundational difference in the location of data transformation, several other distinct characteristics and implications arise for both ETL and ELT. The following table provides a comprehensive side-by-side comparison to highlight these nuances.
The origins of ETL are rooted in the early days of data warehousing and the need to consolidate and standardize data from various sources. Over the years, as technology and business needs evolved, so did the processes surrounding data integration and transformation, leading to the emergence of ELT as a complementary approach to ETL.
In the late 1970s and 1980s, as the concept of data warehousing began to take root, the need for a structured method of populating these warehouses led to the emergence of ETL. This era saw businesses increasingly depending on databases. However, many of these databases operated in isolation, creating a demand to consolidate the scattered data for a more holistic analysis.
As the data landscape evolved, becoming increasingly intricate with a myriad of sources and formats, a pressing need arose to standardize the data integration process. This standardization paved the way for ETL processes, adept at extracting data from diverse sources, transforming it into a consistent format, and subsequently loading it into a unified data repository.
Read More: What is ETL? (Extract, Transform, Load)
However, the industry realized that making data transformations while on the journey can cause problems:
With time, as cloud computing and big data technologies became more prevalent, the ETL paradigm began to shift. The compute power and storage capabilities offered by the cloud, combined with the sheer volume and velocity of data, paved the way for the emergence of ELT (Extract, Load, Transform), where transformations are handled directly within the data warehouse. And here is where the ETL vs ELT dichotomy arose.
This shift to ELT was also influenced by the rise of modern, cloud-native data warehouses like Snowflake, BigQuery, Azure, and Redshift. These platforms provided the scalability and performance to handle transformations on massive datasets after they had been loaded.
With the new, fast data cloud warehouses, data professionals are able to perform their transformations in the warehouse itself Below are the advantages:
Data integration is pivotal for businesses to glean insights, foster decision-making, and streamline operations. Both ETL and ELT play crucial roles in this domain, but their application can vary based on specific use cases and requirements.
Since the ETL vs ELT dichotomy still causes confusion, here's a comprehensive look at when and why one might be favored over the other:
In conclusion, while both ETL and ELT offer their unique advantages, it's essential for data teams to align their choice with their specific needs, infrastructure, and long-term data goals. By weighing the pros and cons of each approach and understanding their distinct use cases, organizations can pave the way for efficient, insightful data integration.
Read More: Zero ETL: What's Behind the Hype?
Overall, ETL and ELT share the same goal, but the infrastructure underlying each process is vastly different. Generally, ELT will be the process that best fits modern companies' data needs. However, there are specific use cases where ETL might be the right choice.
Understanding the difference between ETL vs ELT is only the beginning of truly understanding what you can do with data in an ever-changing environment of technology and tools. ETLT is the newest paradigm to explore. Contrary to ELT, ETLT totally decouples the extract and load steps from any potential optional transformation.
Each operational use case is distinct in how it makes use of incoming data. Some could perform sophisticated transformations, while others might not even need to transform at all. As data warehouses advance more and more, we're sure we will continue to evolve the processes and techniques to extract true value from our data.
Read More: Moving Past ETL and ELT: Understanding the EtLT Approach
ETL (Extract, Transform, Load) is a data integration process that extracts data from source systems, transforms it on a staging server to meet specific requirements, and then loads it into a data warehouse. It's best for structured data, smaller datasets, or when compliance and data privacy are critical.
ELT (Extract, Load, Transform) directly loads raw data into a data warehouse and performs transformations within the warehouse itself. This process leverages modern cloud-native tools, offering faster ingestion, scalability, and support for unstructured data types. Unlike ETL, it retains raw data for historical analysis.
ELT thrives in cloud-based environments because it uses the warehouse's native processing power. It supports high data velocity, large volumes, and diverse data types (structured, semi-structured, and unstructured). This makes it ideal for real-time analytics and scalable workflows.
Choose ETL if:
Choose ELT if:
Yes, some organizations combine both methods. ETL may handle sensitive or structured data transformations upfront, while ELT processes unstructured or high-volume data within the warehouse.
Zero ETL eliminates traditional data pipelines by enabling direct integrations between operational databases and analytics platforms. This emerging approach reduces latency and simplifies architecture but may not fully replace ETL or ELT in complex use cases yet.