Extract, Load, Transform (ELT)

What is ELT and how it works?

ELT, which stands for Extract, Load, Transform, is a contemporary approach to data integration that has gained traction in recent years. Unlike the traditional ETL approach, ELT prioritizes loading raw data directly into a data warehouse, followed by the necessary transformations based on specific application requirements.

Understanding the ELT Process:

  • Extraction: Just like in ETL, the first step in ELT is the extraction of data from various source systems. This could be databases, applications, files, or any other data storage platforms.
  • Loading: Unlike ETL, where data undergoes transformation before being loaded, ELT prioritizes direct loading of raw, unprocessed data into the target system, typically a data warehouse. This approach ensures data remains unaltered and authentic upon entry, ready for on-the-spot adjustments based on the evolving needs of data structures or types.
  • Transformation: With ELT, the transformation magic happens after loading. Now securely within the data warehouse, data is then processed and reshaped, often employing tools like dbt. The transformation is tailored to align with specific business reporting and analytics requirements.
The ELT methodology distinguishes itself by placing transformation at the process's end. This approach empowers organizations to populate data lakes with unstructured raw data and facilitates instantaneous data extraction and loading.
A significant driver behind ELT's popularity is the rise of cloud-based data warehouses. These modern solutions, predominantly hosted, offer unparalleled scalability without the overheads of hardware or software management. With the cloud providers handling the heavy lifting, businesses can swiftly deploy and configure their data repositories.


Main Differences Between ELT and ETL

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are both prominent data integration strategies used to centralize data from varied sources into a single repository. However, they differ in key areas, affecting the way businesses undertake data integration:
  • Process Sequence:
    • ELT: Data extraction remains the same, but transformation occurs after loading the data into the target system.
    • ETL: Data is first extracted, then transformed to meet the specifications of the target system, and finally loaded into the central storage.
  • Role of the Target System:
    • ELT: The transformation happens within the target system, capitalizing on its inherent processing and storage capabilities, eliminating the need for third-party tools.
    • ETL: The transformation is carried out before data enters the target system. This demands that data is already compatible with the system's schema and requirements.
  • Data Complexity Handling:
    • ELT: Tailored for semi-structured or unstructured data, offering greater processing flexibility for less rigidly defined datasets.
    • ETL: Best suited for structured data, which comes pre-organized in specific formats.
  • Cost Implications:
    • ELT: Can be a more affordable option given its reliance on widely available tools like SQL.
    • ETL: May have a higher cost, considering licensing, and infrastructure. However, it can be economical when processing large chunks of structured data.
In essence, while both ETL and ELT have their merits, their unique attributes necessitate thoughtful consideration based on data complexity, the capabilities of the target system, and budgetary constraints. The choice between ETL and ELT will hinge on a business's specific data processing requirements.


When to Use ELT vs. ETL:

  • Data Volume:
    • ELT: Favours real-time data processing and analysis, particularly for smaller data volumes.
    • ETL: Ideal for batch processing extensive datasets, optimizing efficiency, and ensuring the target system isn't swamped.
  • Data Complexity:
    • ELT: Emerges as the top choice for less defined, semi-structured, or unstructured data sources such as logs and social media content.
    • ETL: Prefers structured data, like relational databases, enabling easier and automated transformations.
  • Data Latency:
    • ELT: Accelerates processing by immediately loading data and subsequently transforming it within the system.
    • ETL: May result in prolonged processing times since transformation precedes loading.
  • Budgetary Considerations:
    • ELT: Offers cost savings by leveraging standard tools like SQL, which are straightforward to set up and manage.
    • ETL: Can be pricier due to the need for specialized software and associated upkeep costs.
The decision between ETL and ELT should be informed by a careful assessment of data volume, complexity, required processing speed, and budget. The goal is to adopt a strategy that optimally supports your data integration and analytics objectives.