by Alex Pauncz | July 06, 2021

ETL vs. ELT: Which is Right for Your Data Warehouse?

To bring their diffuse data into their data warehouse, organizations typically leverage an ETL or ELT process using a dedicated data pipeline. In this article, we define and compare ETL and ELT processes to help you determine which is right in various data integration scenarios.


What are ETL and ELT?

Typically, organizations bring data from different data sources and databases into a data warehouse or data lake using the extract, transform, and load (ETL) process. The three parts of ETL involve:

  • Extract: Replicate data from source systems.
  • Transform: Standardize the replicated data from the varying formats used across multiple data sources into the common data model used by the destination data warehouse.
  • Load: Pipe the newly formatted data into the target data warehouse or data lake

In the early days of ETL, when data warehouses had limited resources, transforming data before it reached the warehouse allowed organizations to limit the use of valuable storage, computation, and bandwidth resources across the workflow.

Today, modern high-speed, cloud-based data warehouses and data lakes can store virtually limitless volumes of data and offer scalable processing power. These technology developments have enabled a new data integration architecture called extract, load, transform (ELT), in which data is immediately loaded into the data warehouse or data lake after extraction and transformed only when users are ready to employ the data for analysis.

The Pros and Cons of ETL and ELT

While ELT is a popular topic nowadays, it is not necessarily the ‘better' solution. Both ETL and ELT have their strengths and weaknesses, each providing greater value for some use cases than others.

Advantages of ETL

ETL offers a superior service for organizations that must meet regulatory requirements for securing sensitive data. It is also more cost-effective for those with massive amounts of data.

  • Greater Compliance

    Companies subject to data privacy regulations, such as the GDPR, HIPAA, or the California Consumer Privacy Act (CCPA), need to remove, mask, or encrypt specific data fields to protect the privacy of their customers. ETL provides greater data security because it performs transformations that safeguard private data before putting the data in the data warehouse. This data security prevents system admins from accessing the sensitive information through logs in the data warehouse.

  • Reduced Storage Costs

    Because ETL only transfers data that has been transformed into the data warehouse, organization can save on storage costs by storing only the data they need within their warehouse. In contrast, ELT loads all your data into the data warehouse, including data you don't need.

ETL Disadvantages

Every ETL transformation is dictated by the specific needs of data analysts, meaning every process requires a bespoke pipeline. Because data pipelines perform sophisticated transformations tailored to the analytics needs of end users, they require a dedicated team of engineers to build and maintain custom code.

The added development takes time, makes adding data sources difficult, and limits scalability. Worse, the process is brittle; any change to upstream schemas or downstream data models can break the pipeline and require custom code revisions.

Advantages of ELT

ELT is known for delivering greater flexibility, less complexity, faster data ingestion, and the ability to transform only the data you need for a specific type of analysis.

  • Greater Flexibility

    Unlike ETL, ELT does not require you to develop complex pipelines before data is ingested. You simply save all your data in the data warehouse without having to transform and structure it first, and immediately access all your information.

  • Simplicity

    SQL databases offer many built-in capabilities for querying and manipulating data. Modern ELT solutions can leverage these native capabilities to transform the data after it is loaded to the warehouse. This makes it easy for enterprise IT teams to manage data transformations using the built-in SQL processes inside databases like SQL Server.

  • Rapid Data Ingestion

    Because it does not require you to transform data to a special format before saving it in the data warehouse or data lake, ELT can instantly ingest data. Users no longer need to wait for data to be cleansed or modified.

  • Transform Only the Data you Need

    With ELT, users need only transform the data required for a specific analysis, and they can flexibly transform the data in different ways to produce specific metrics, forecasts, and reports. In contrast, ETL requires the modification of the entire pipeline if the previously decided structure doesn't allow for new types of analysis.

ELT Disadvantages

While ELT is great for organizations who need to manage large amounts of unstructured data, these solutions are less compliant and reliable compared to their ETL counterparts.

  • More Vulnerable to Risk

    Because ELT requires you to upload sensitive data before transforming it, the process exposes private data in logs that are accessible to your system admins. In addition, using ELT to transform data can inadvertently violate GDPR compliance standards if non-compliant data leaves the European Union when data is uploaded to the data warehouse or data lake.

  • Less Established

    The tools and systems of ELT are still evolving, which means they are not as reliable as ETL. Moreover, while ETL takes more up-front effort to set up, its data structuring delivers more accurate insights than ELT.

A Hybrid Approach To Data Movement (ETLT?)

As we've seen, ETL better supports use cases that demand reliability and compliance, while ELT excels when users need their transformations to be fast, flexible, and simple.

But some scenarios benefit from employing a combination of ETL and ELT. For example, you might want to take advantage of ELT's rapid ingesting to give your data analysts immediate data access with the flexibility to change analytics data models on the fly. At the same time, you may need to meet data security and compliance requirements that require you to mask, remove, or encrypt PHI and PII before moving data into the data warehouse. ETLT (extract, transform, load, transform) is an emerging framework that combines the best of both worlds.

ETLT integrates data into the data warehouse or data lake by:

  • Extracting raw data from source applications and databases and loading it into a staging area.
  • Lightly transforming the data in the staging area to remove, mask, and encrypt sensitive data. These transformations occur quickly because they only transform one source at a time.
  • Loading the data into the data warehouse.
  • Transforming and integrating the data more completely within the data warehouse, using database and SQL commands to process the transactions. This second transformation step performs the tasks necessary to consolidate data from multiple sources.

Taken together, this combination process enables IT teams to flexibly meet any need.

CData Sync: ETL & ELT Pipeline for Every Integration Scenario

Whether you need ETL, ELT or ETLT, you can handle all your data integrations with our dedicated data pipeline, CData Sync. CData Sync can perform the necessary transformations from 250+ popular databases and data sources and replicate it to any data warehouse or lake.

For more information, explore our full suite of data integration & ETL/ELT solutions or book a free consultation with a CData integration specialist.


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