three most common transformations in etl processes

Security and privacy: ETL processes often involve sensitive or confidential data. what the original source was) can be difficult once data sources are integrated.Īvailability and scale: Is there enough storage and compute in your staging area to keep up with the data? (The more data that needs to be transformed, the more computationally and storage intensive it can become.)įiltering: Which data is important data and which can be ignored or discarded?

Reasoning about the lineage of data (i.e. It can be difficult to identify and correct errors or inconsistencies in the data.

Permissions: Do your networks and systems have access and rights to the data?ĭata freshness: Are you capturing real-time data, or stale data that's no longer of value? What is the ephemeral nature of the data? Are you able to capture it before the data passes its lifetime?ĭata quality and integrity: Do you have validation in place to notice if the data that is extracted is in an expected form? Combining data from multiple sources can be challenging due to differences in data formats, structures, and definitions.

Three most common transformations in etl processes