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ETL vs ELT: Key Differences Explained

ETL extracts raw data from sources, transforms it on a secondary server, and loads it into a target database. ELT loads raw data directly into a data warehouse, where data cleansing, enrichment, and transformations occur. While ETL has been used longer, ELT allows for greater flexibility and speed by performing transformations within the data warehouse. Both methods have advantages and disadvantages around compliance, costs, maintenance needs, and security.

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Miguel Angel Piñon
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202 views7 pages

ETL vs ELT: Key Differences Explained

ETL extracts raw data from sources, transforms it on a secondary server, and loads it into a target database. ELT loads raw data directly into a data warehouse, where data cleansing, enrichment, and transformations occur. While ETL has been used longer, ELT allows for greater flexibility and speed by performing transformations within the data warehouse. Both methods have advantages and disadvantages around compliance, costs, maintenance needs, and security.

Uploaded by

Miguel Angel Piñon
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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ETL vs ELT

What’s the
Difference?

by ginacostag
What is ETL?

ETL
Data Warehouse
Extract Load

Load
Transform DWH

Extract, transform, and load (ETL) is a data integration


methodology that extracts raw data from sources, transforms
the data on a secondary processing server, and then loads
the data into a target database.

ginacostag
What is ELT?
ELT
DWH

Extract Transform

Load

ELT (extract, load, and transform) loads raw data directly


into a target data warehouse. Data cleansing, enrichment,
and data transformation all occur inside the data
warehouse itself. Raw data is stored indefinitely in the data
warehouse, allowing for multiple transformations.

ginacostag
What is ELT?

Cloud-based data warehouses offer near-endless storage


capabilities and scalable processing power. For example,
platforms like Amazon Redshift and Google BigQuery
make ELT pipelines possible because of their incredible
processing capabilities.

ginacostag
ETL

Maturity. ETL was developed first and has been in practice for more
than two decades. This means that there are more engineers with
experience in ETL implementations and more ETL tools in the
marketplace to build data pipelines within organizations.
Advantages
Compliance. ETL transforms data before it reaches its destination.
When companies are subject to data privacy regulations such as GDPR,
ETL allows them to remove, mask, or encrypt sensitive data before it's
loaded to the data warehouse to ensure compliance.

Frequent maintenance. ETL data pipelines handle both extraction and


transformation. But they have to undergo refactors if analysts require
different data types or if the source systems start to produce data with
Drawbacks deviating formats and schemas.

Higher upfront cost. Defining business logic and transformations can


increase the scope of a data integration project.

ginacostag
ELT

High speed. ELT allows for all of the data to go into the system
immediately, and from there, users can determine the exact data they
need to both transform and analyze.

Lower cost. Requires a less-powerful server for data transformation and


Advantages takes advantage of resources already in the warehouse. This results in cost
savings and resource efficiencies.

Flexibility. Analysts no longer have to determine what insights and data


types they need in advance but can perform transformations on the data
as needed in the warehouse.

Segurity gaps. Storing all the data and making it accessible to various
users and applications come with security risks. Companies must take steps
to ensure their target systems are secure by properly masking and
Drawbacks
encrypting data.

Increased latency. The need to continually transform data slows down


the overall time it takes to perform queries/analysis.

ginacostag
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