When companies collect data, it usually comes from many sources — websites, apps, CRMs, or even Excel sheets. The problem is, this data is rarely clean. Dates may be stored in different formats, customer names may be duplicated, and some fields may even be missing. To fix this, businesses use the ETL process in data warehouse.
ETL means Extract, Transform, Load:
Extract – pull raw data from different systems.
Transform – clean and standardise it (remove duplicates, correct errors, format dates, calculate values).
Load – store the prepared data into a warehouse like Snowflake, BigQuery, or Redshift.
Think of it like building a software product: extraction is fetching the raw code libraries, transformation is debugging and optimising them, and loading is deploying the final app.
Without ETL, every reporting task would require manual cleanup, wasting time and leading to bad insights. With ETL, businesses can generate dashboards, run analytics, and even train machine learning models on high-quality data.
Example: An Indian clothing brand collects sales data from its app, website, and Amazon store. Using ETL, it standardises product names, converts timestamps to IST, and loads everything into Google BigQuery. This gives the team accurate insights on top-selling products and delivery performance.
For anyone starting in Data Science, Analytics, or Digital Marketing, ETL is a fundamental skill. At Ze Learning Labb, we teach how to build and run ETL pipelines step by step.
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