ETL vs ELT: what you need to know
ETL (extract, transform, load) has been the traditional approach for data analytics and warehousing for the last couple of decades. But with the introduction of cloud technologies, ELT (extract, load, transform), also known as ‘ingest, store, transform’, is a more modern approach to data processing that offers some organisations more speed and fluidity.
What’s the difference?
- ETL: data is extracted from a source system, transformed on a secondary processing server and loaded into a destination system. Every time you want to update a data point, you must go back to the source to extract, transform then load all the data.
- ELT: data is extracted from a source system, loaded into a destination system and transformed inside the destination system. If you want to update your data, the platform only looks for the data points you want to update, not all the data.
The challenges of more data and source types
According to a whitepaper published by Aberdeen and Sisense, survey respondents use an average of 30 unique data sources regularly, and 40% of respondents analyse unstructured data from both internal and external sources.
Doing that efficiently requires a platform that supports structured and unstructured data from multiple sources and in large volumes.