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Data & AI

ETL is a data integration process in which data is extracted from one or more source systems, transformed into a consistent and clean format, and loaded into a destination system such as a data warehouse for analysis.

The Extract phase connects to source systems — relational databases, REST APIs, flat files, CRM exports, or streaming platforms — and retrieves raw data on a scheduled or event-driven basis. The Transform phase applies business rules: deduplicating records, standardising date formats and currency codes, joining datasets from different sources, computing derived fields, and filtering out irrelevant or corrupt rows. The Load phase writes the cleaned, structured data to the target system, typically a data warehouse, using full loads (replacing the entire dataset) or incremental loads (appending or updating only changed records since the last run). Modern data teams often favour the ELT pattern (Extract, Load, Transform) where raw data is loaded into a cloud data warehouse first and transformations are performed using SQL inside the warehouse — using tools like dbt — leveraging the warehouse's own massive parallel processing power.

Example

A logistics company runs a nightly ETL pipeline that extracts shipment records from its operational PostgreSQL database, transforms them by geocoding addresses and computing delivery SLA adherence, and loads the results into BigQuery for next-morning BI reports.

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