What is the difference between ELT and ETL?

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In a data-driven world, organizations make critical choices in collecting, storing, and using data. Decisions by data engineers impact costs and compliance. To reduce headwinds later in the data pipeline, how data management is automated and transformed during the preparation stage is important. For security telemetry and data, which traditionally has been kept separate or selectively shared, optimizing data and extracting value requires data teams to plan creatively that breaks down silos to create a consistent data set that facilitates trust and operational efficiencies.

There are two popular data integration processes: 

  • Extract, transform, load (ETL) 
  • Extract, load, and transform (ELT)

The key differentiator for these two processes is when the transformation of the data takes place in the data processing pipeline. Ultimately, this can affect how data is used in data processing, analytics, and business intelligence tools.  

What do the words extract, transform, and load mean in data science? 

Both data integration methods engage in the same processes but in a different order: 

  • Extract: Reading and collecting raw data from various sources, like Software-as-a-Service (SaaS) applications, endpoint detection and remediation (EDR) tools, or domain name servers (DNS) logs 
  • Transform: Parsing, cleaning, standardizing, and correlating the data’s format or structure to match the desired structure for the intended purpose. 
  • Load: Placing the data in the target system or repository, like a data lake

The data pipeline consists of the tools and processes that automate data movement and transformation from the source system to one or more repository or destinations where it can be stored, analyzed, and accessed by business intelligence (BI) tools.  

What is the difference between ELT and ETL? 

At a basic level, the two data integration methods differ in the order and location of the data transformation step during the data integration process. Both methods are designed to move data from its source or sources to its destinations, however the changing of the order can impact costs, change the tools and underlying infrastructure that can affect data volume, processing power, transformation complexity, and how quickly the data is available for use. 

Transformation 

By swapping the order of operations, the transformation process changes location: 

  • ELT: Data is loaded into a repository like data warehouse or data lake or other destination systems in its raw form, where then transformation is performed in the target repository and on an as-needed or on-demand basis 
  • ETL: Transformation is performed in a staging area or "on the fly" where it is cleansed and enriched, and the repository or destination system receives the data in an analysis-ready format.

Data that undergoes the ELT process will be faster to land and made available in the repository. However, data processed in this way will require intensive process and manipulation by the data user before it can used effectively for analysis and reporting. Another consideration is the security and privacy controls that need to be in place where the raw data is loaded.

Meanwhile, ETL may have more uplift in data preparation and manipulation at first, data stewards can have more control over data quality and consistency. Additionally, data architects can help accelerate time to value for data engineers and analysts while managing complex transformations with more ease such as ensuring data privacy and compliance.  

Load 

Changing where the transformation occurs also impacts how the data lands in its target repository: 

  • ELT: raw, structured, semi-structured, and unstructured data in repository that needs to be mapped and standardized before use or analysis 
  • ETL: mapped and standardized data and, depending on the organization, raw data in the repository, where the mapped and standardized data is ready to be used and analyzed  

Advantages of ETL and ELT 

Many data teams find choosing between ETL and ELT a no-win situation. Both data integration methods have valuable benefits depending on how the organization wants to use the data and its data maturity journey, meaning that neither is a comprehensively ideal choice.  

ETL 

ETL’s primary objective is to prepare data for analytics. By transforming the data before landing it in the data repository, ETL enables: 

  • Data accuracy: address data issues by identifying and fixing data errors, anomalies, and inconsistencies early in the process to reduce impact on downstream processes and analytics  
  • Scalability: accommodate increasing data requirements by transforming data before loading it into a repository, like for investigating security incidents that may cause a surge in data generation and use  
  • Data quality: improve analytics by validating, cleaning, and standardizing data before loading it into a repository so it is ready for use when people need it 
  • Compliance: identify and/or remove sensitive data earlier to enable storage in appropriate geographic regions and with appropriate controls 
  • Storage optimization: reduce data duplication/replication and maintain a single source of truth when storing data in one or multiple repositories for different use cases or for data sharing 
  • Analysis speed: pre-define use cases for structured and transformed datasets for immediate analysis once loaded into repository  
ELT 

ELT’s primary objective is to land data in the repository to leverage its power to perform the transformation. By loading data before transforming it, ELT enables: 

  • Performance: use data warehouse or lake to perform transformation for improved data integration performance, especially for large, complex datasets 
  • Flexibility: perform complex and dynamic transformation with built-in target system functions and capabilities 
  • Speed: ingest and load data faster by doing the transformation process inside the system rather than using an intermediary step 
  • Data governance: track and audit data access and use more effectively by storing raw data before transforming it 
  • Initial costs: automate data onboarding process without planning all transformation before moving data 
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