ETL v/s ELT - 14 Major Differences

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Check out this article highlighting 14 Major Differences between ETL and ELT

:zap: Here are the major highlights from the article:

Feature ETL ELT
Process Order After extraction data is transformed and then loaded. After extraction data is first loaded into the target system and then transformed.
Maintenance Requires similar maintenance as ELT Requires similar maintenance as compared to ETL but with additional considerations.
Cost Usually costs more Less expensive and saves on data egress costs as well.
Security It is more secure as compared to ELT as transformation is performed on a separate server Data transformation is performed in the target server only which is comparatively less secure.
Hardware Requirements Traditional ETL processes require costly hardware. ELT process is innately cloud-based no extra hardware is required.
Support for Data Lake Not suitable for data lakes. It is appropriate for data lakes due to the high storage needs.
Support for Data Warehouse It is highly preferred for loading structured data after transformation It is supported but not preferred because unstructured data is loaded first.
Performance It is preferred when working with small datasets. It is preferred when working with larger datasets.
Data Movement It requires more movement of data. It requires less movement of data.
Flexibility and Scalability Addition of new fields after data loading is difficult. New fields or components can be added very easily even after the data is loaded.
Support for unstructured data It has limited support for unstructured data. Transformation of unstructured data is easily supported even after loading.
Data Latency It has higher data latency. It has lower data latency.
Data Loss Chances of data loss are high during the transformation phase Data loss chances are less as data is loaded first and then transformed.
Maturity It is a traditional approach It is a modern approach and is evolving with time.

To learn more about these differences, checkout the full article here⤵️

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