ETL vs. ELT: Which is better? 6 key differences

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 the six main differences between 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 ETL (extract, transform, load) and/or ELT (extract, load, transform) processes. The three parts of ETL and ELT are the same, though performed in a different order:

• 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 immense volumes of data and offer scalable processing power. These technology developments have enabled a new data integration architecture called 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 General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (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 sensitive information through logs in the data warehouse.
• Reduced storage costs: Because ETL only transfers data that has been transformed into the data warehouse, organizations 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.

ETL use cases

ETL processes are most useful when the format and structure of the data in the final system of record is a priority.

• Data warehousing: ETL is extensively used for populating data warehouses. Data from various sources is extracted, transformed to fit the data warehouse schema, and loaded into the warehouse, enabling efficient reporting and analysis.
• Data migration: When organizations need to migrate data from old systems to new systems, ETL processes are used to extract data from the old system, transform it to fit the new system's format, and load it into the new system.
• Data integration for mergers and acquisitions: When companies merge or are acquired, ETL is used to integrate disparate data systems, ensuring a seamless data ecosystem across the newly formed entity.

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 that 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.

ELT use cases

ELT processes are most useful when the speed of data delivery is a priority. Popular ELT use cases include:

• Big data analytics: ELT is ideal for big data environments like Hadoop or cloud-based data warehouses (e.g., Snowflake, Google BigQuery) where massive volumes of data can be loaded and then transformed using the powerful processing capabilities of these platforms.
• Streamlined data lake usage: ELT is often used in data lake environments where data is loaded in its raw form and then transformed as needed for various analytics or reporting purposes, allowing for more flexibility in data usage and exploration.
• Lowering processing overhead: By shifting the transformation workload to powerful cloud data warehouses or big data platforms, ELT can reduce the processing load on source systems, which is beneficial for operational systems that cannot afford performance degradation.

Get more information about ETL and 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 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 to mask, remove, or encrypt protected health information (PHI) and personally identifiable information (PII) before moving data into the data warehouse.

ETLT (extract, transform, load, transform) is an emerging framework that combines the best of both worlds and 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 and ELT pipeline for every integration scenario

Whether you need ETL, ELT, or ETLT, you can handle all your data integrations with CData Sync. CData Sync is a dedicated pipeline that can handle transformations from 250+ popular databases and data sources and replicate them to any data warehouse or lake.

For more information, explore our full suite of data integration and ETL/ELT solutions, or try CData Sync for free today.