LinkedIn Learning : ETL in Python and SQL
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Learned to design and build end-to-end ETL (Extract, Transform, Load) pipelines using both Python and SQL
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Gained hands-on experience extracting data from multiple sources, cleaning and transforming it with pandas, and loading it into target systems such as relational databases and data warehouses
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Developed skills in data quality checks, validation, and automation, ensuring accuracy and reliability across data workflows
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Explored ETL job scheduling and orchestration using Python and Apache Airflow, reinforcing automation practices in modern data engineering
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Tools & Techniques:
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Python & pandas – data extraction, cleaning, transformation, and standardization
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SQL – data validation, quality assurance, and loading into relational databases
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Apache Airflow – automation and scheduling of ETL workflows
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ETL Architecture – designing scalable data pipelines and understanding data warehouse/lake integration
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Data Quality Management – duplicate handling, missing value imputation, and consistency validation