ETL (Extract, Transform, Load) pipelines are critical for moving data from diverse sources into a central warehouse for analysis. Modern ETL tools automate the extraction of structured and unstructured data, apply transformations like cleaning, validation, and enrichment, and load it efficiently into cloud platforms such as AWS Redshift, Google BigQuery, or Snowflake.
By implementing robust ETL pipelines, organizations can ensure data quality, reduce processing time, and enable real-time analytics. For example, applying schema validation during the transformation stage prevents downstream errors in dashboards, while incremental loading improves pipeline performance.
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