What is ELT?

Extract, load and transform, or ELT, is a data integration process that moves data directly from source to destination for analysts to transform as needed.
March 30, 2022

ELT means Extract, Load and Transform, which are the essential stages in the process of data integration. It is commonly contrasted with ETL (Extract, Transform and Load), which is likewise a common approach to data integration.

The three steps to ELT are:

  • Extract – Raw data is copied from applications, databases, files and other data sources.
  • Load – Data is copied to a central destination, usually a data warehouse.
  • Transform – Transformations include all operations that alter or create values, such as cleaning, summarizing, pivoting and combining data. These operations can be complicated and usually obscure or alter the raw values from the source.

The goal is to turn raw data into a unified view of an organization’s operations for the purposes of business intelligence and analytics.


ETL dates from the 1970s, when storage, computation and bandwidth were scarce. By transforming data before loading it, ETL limits the volume of data that is warehoused in order to preserve storage, computation and bandwidth resources. Generations of data engineers have developed and used ETL tools and it remains the norm in data integration.

The critical weakness of traditional ETL tools, such as Pentaho, Informatica and SSIS is that data is transformed before it is loaded. This not only obscures the raw, original values, but also means that the entire pipeline must be rebuilt every time either the source changes or analysts need a new data model. 

Concretely, this means stoppages and engineering time whenever your data source adds a new field or your company needs a new metric. It is good for data sources to add more data and for your company to track new metrics, but the brittleness of ETL turns the opportunities offered by new data into a heavy technical burden. 

By contrast, the ELT process extracts and loads raw data with no assumptions about how the data will be used. ELT is a product of the modern cloud, enabled by the plummeting costs of storage, computation and bandwidth over time. Without constraints on storage, computation and bandwidth, there is no reason to follow a data integration architecture that preserves those resources.

Moving transformation to the end of the process turns transformation from an engineering-centric activity baked into data pipelines to an analyst-centric activity performed in a data warehouse. This means that changes to downstream business and analytics requirements, i.e. data models for dashboards and reporting, don’t affect the pipeline. 

Since ELT makes no assumptions about how data will be used, the data models that populate the destination can be standardized for all users. Standardized schemas mean the ELT pipeline can be outsourced and, from the vantage point of the user, automated.

Read more about the differences between ETL and ELT in detail.

The promise of fully automated ELT

With a fully-managed, outsourced and automated solution, it becomes a provider’s SLA to continuously adapt your data pipelines to changes at the source and provide your analysts with fresh data. A cloud-based solution also makes infrastructure easy to scale as needed, obviating the need to build on-premise infrastructure. With a good security SLA, you also won’t need to home brew security protocols.

From the user’s standpoint, allowing an outside vendor to build and maintain a data pipeline automates pipeline maintenance tasks including, but not limited to:

  • Modifying data extraction scripts to accommodate frequent API changes
  • Normalizing data source extracts into a queryable schema
  • Keeping that schema updated with new data or new structures as they are created in the data source

Choosing a data integration tool that features fully managed ELT eliminates these technical considerations and frees your data team from designing, building and maintaining a complex piece of software. This saves you engineering time and enables your analysts to reliably and rapidly access their data.

With fully managed ELT, all of your data from your various sources are centralized in one place and continuously updated without human intervention. This persistent, up-to-date data repository gives your teams the bandwidth to tackle new questions as they emerge, accelerate project timelines and create new insights from existing datasets. Fully managed ELT takes this another step further by updating incrementally, resulting in faster turnaround and less impact to production source systems.

Buy, don’t build

Providers of purpose-built, fully managed ELT have taken pains to understand every idiosyncrasy of the data sources they connect to and regularly pressure-test their software against a variety of use cases. The design considerations involved in developing a robust ELT solution are not trivial and the cost in time, money, labor and morale is substantial.

Outsourcing your data integration allows you to rapidly narrow time-to-insight and saves you valuable engineering resources both for the initial build and ongoing maintenance. Automated ELT means automated data integration at any level of technical ability, empowering your analysts to work with minimal delay.

How to get started with fully managed ELT 

All it takes to set up integrations is to provide or obtain a user account(s) with the appropriate access levels. This is typically a one step process. From there, automated data connectors will do a full historical load of all the data available in the data source and will keep that dataset updated as changes are read from the source. Integrations can be set up in minutes and you’ll never have to check back on the pipeline as your data source evolves.

To learn more, explore this FAQ about how to take advantage of a fully managed ELT solution.  

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