Data Engineering Resources


Organizations across the world leverage ETL and ELT as a cornerstone of data integration and data warehousing strategies. While most companies have historically used ETL for data integration, ELT is a more modern approach.

What is ETL?

ETL is a type of data integration process using three distinct but interrelated steps, Extract, Transform, Load, used to synthesize data from multiple sources into destination data warehouses, data lakes, and databases.

  • Extraction :This first step involves copying data from the source system
  • Transformation :ETL data transformations involve converting data from the formats used by the original data source to fit the data model used by the end database destination.
  • Loading :During the loading step, the pipeline replicates data from the source into the target system, which might be a data warehouse or data lake.

What is ELT?

ELT stands for "extract, load, and transform" and is the process a data pipeline uses to replicate data from data sources and applications into a target database or data warehousing system. ELT is similar to ETL but leverages the processing power of modern databases and warehouses to handle data transformations after the data is already loaded into the destination database.

In the ELT approach, after you've extracted your data, you immediately start the loading phase - moving all the data sources into a single, centralized data repository. With today's infrastructure technologies using the cloud, systems can now support large storage and scalable computing. Therefore, a large, expanding data pool and fast processing is perfect for maintaining all the extracted raw data.


ETL is the traditional process historically leveraged by thousands of organizations across the world to handle data replications for data integration. But the main drawback of ETL is its lack of speed, as data would have to be fully transformed before it can be placed into target databases. ELT is now the standard process for organizations handling most data integrations, as enterprise data warehousing systems deliver unprecedented, raw processing power. Some additional benefits of ELT include:

  • With ELT, businesses can use the processing engines in the destinations to efficiently transform data within the target system itself.
  • Since transformations happen inside the data system itself, no staging area is required.
  • ELT can load all data immediately, and users can determine later which data to transform and analyze.

Not every database supports ELT, as certain databases are more specialized or offer only limited processing capabilities, so ETL is still common. But in most cases, ELT is preferrable.

Benefits of ETL/ELT

The ETL/ELT process offers several advantages over other data integration methods, particularly with the right ETL/ELT solution:

  • Automated Data Pipeline - reduces errors and enables automated, scheduled data replications to save time and manpower
  • Unstructured Data Support - offers a pipeline for data lakes to ingest structured, unstructured, semi-structured, and raw data types
  • Petabyte-Scale Replications - built to support replications for massive amounts of data
  • Cloud-Ready - works with cloud-based data warehousing solutions to support cloud data integration

ETL/ELT also provides for core data warehousing initiatives, a core enterprise function growing in importance. The primary benefit of the ETL/ELT process is unlocking the benefits of data warehousing, which:

  • Offers deep historical context for business
  • Enhances business intelligence solutions for decision-making
  • Enables context and data aggregations so businesses can generate higher revenue and/or save money
  • Enables a common data repository
  • Allows verification of data transformation, aggregation, and calculation rules
  • Supports sample data comparison between source and target systems
  • Helps to improve productivity as it codifies and reuses without additional technical skills

Learn more about the benefits of data warehousing

Modern Data Pipelines

Want to learn more about data pipeline technologies for data replication and data warehousing?Check out our webinar on Data Pipeline Strategies for Modern Data Warehousing with Forrester Analyst Noel Yuhanna.

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