Technical GlossaryData Engineering and AI Infrastructure

Data Lifecycle Tiering

An approach in which data is moved across storage tiers as its access frequency, age, and business value change.

Data lifecycle tiering is used in data lakes to manage both storage cost and performance. Recent and frequently accessed data is kept in faster tiers, while older and less frequently needed data can be moved to cheaper layers. This is not only a storage optimization strategy, but also a way of managing the time dimension of data strategy. Without proper tiering, costs can escalate quickly.

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ETL

A classic data integration approach in which data is extracted from source systems, transformed, and then loaded into a target analytical environment.

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ELT

A modern approach in which data is loaded into the target platform first, and transformations are performed later inside the storage or compute layer.

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Reverse ETL

An integration approach that moves curated data from analytics platforms back into operational systems.

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Data Lifecycle Tiering

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