Comparison table of ETL vs ELT

etl-vs-elt.md

Parameters	ETL	ELT
process	data is transformed at staging server and then transferred to Datawarehouse DB	data remains in the DB of the Datawarehouse
code usage	compute-intensive transformations and for small amount of data	high amounts of data
transformation	done in server/staging area	performed in the target system
time-load	data first loaded into staging and later loaded into target system (time intensive)	data loaded into target system only once (faster)
time-transformation	needs to wait for transformation to complete. As data size grows, transformation time increases	speed is never dependant on the size of the data
time- maintenance	needs high maintenance for selecting data to load and transform	low maintenance as data is always available
implementation complexity	at an early stage, easier to implement	requires deep knowledge of tools and expert skills
support for data warehouse	used for on-premises, relational and structured data	used in scalable cloud infrastructure which supports structured, unstructured data sources
support for lake/mart/lakehouse	no	yes
complexity	oads only the important data, as identified at design time	involves development from the output-backward and loading only relevant data
cost	high costs for small and medium businesses	low entry costs using online SasS Platforms
lookups	both facts and dimensions need to be available in staging area	all data will be available because extract and load occur in one single action
aggregations	complexity increase with the additional amount of data in the dataset	power of the target platform can process significant amount of data quickly
calculations	overwrites existing column or need to append the dataset and push to the target platform	easily add the calculated column to the existing table
maturity	mature and well documented	relatively new concept and complex to implement
hardware	most tools have unique hardware requirements that are expensive	being Saas hardware cost is not an issue
support unstructured data	mostly supports relational data	supports
size/type of data set	best for small relational data sets with complex transformations relevant to analysis goals	can process any big data, structured or unstructured
compliance/privacy	better for GDPR, HIPAA, and CCPA compliance since sensitive data can be omitted before loading	having more risk of exposing private data
data output	tipically structured	structured, semi-structured, unstructured