ETL and ELT
Patterns for moving data from sources to analytical stores: ETL transforms before loading; ELT loads first.
ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are the two primary patterns for moving data from operational sources into analytical stores. ETL transforms data before loading and was dominant in the on-premise era when storage was expensive. ELT loads raw data first, then transforms inside the cloud data warehouse where compute is elastic and cheap. Modern data stacks favor ELT with tools like Fivetran/Airbyte for extraction-and-load, then dbt for in-warehouse transformation. Empire325 builds modern ELT pipelines with rigorous testing, documentation, and CI/CD, ensuring transformations are version-controlled and auditable.
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Data Transformation
Data warehousing, attribution modeling, and analytics pipelines that unify marketing, sales, and product telemetry.
Explore Data Transformation →Related terms
Data Warehouse
A centralized repository of structured, integrated data from multiple sources, optimized for analytics.
First-Party Data
Customer data a company collects directly from its own properties, apps, and interactions.
Customer Data Platform (CDP)
Software that unifies customer data from multiple sources into persistent, accessible profiles.
Reverse ETL
Operationalizing data warehouse insights by syncing them back into business tools and SaaS platforms.