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What Is Data Transformation?
Last week, we discussed how to build a data warehouse — covering the full lifecycle from data ingestion to storage and access. Today, we’re zooming in on one of the most crucial stages in that journey: data transformation.
In this video, we’ll break down how raw, messy data becomes clean, standardized, and analysis-ready — an essential step before data enters your warehouse or analytics systems.
What you’ll learn in this video:
• Why data transformation is like baking a cake (yes, really)
• How to clean and standardize your data for consistency
• Key transformation techniques: normalization, aggregation, derivation
• Strategies for handling missing data (imputation, removal, flagging)
• How validation and quality checks ensure your data’s reliability
• The roles of standardization and normalization in a warehouse-ready dataset
If you’re working with data pipelines, warehousing, or analytics — this video is your foundation for doing it right. Whether you're a data engineer, analyst, or aspiring data scientist, you'll walk away with a clear understanding of how to turn raw data into valuable insights.
If you want to see where transformation fits in the big picture, check out the previous video on “How to Build a Data Warehouse”.
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