Build a strong practical foundation in modern data engineering using Python, PostgreSQL, SQL, Git, GitHub, and common engineering data formats. You will learn how data engineers organize development environments, work with structured data, design relational models, build reliable file-to-database workflows, and prepare trusted datasets for analysis.

You will begin by exploring the role of data engineering in modern data systems, the data engineering lifecycle, and the differences between batch and real-time processing. You will also examine important design considerations such as latency, throughput, scalability, and system boundaries. Through guided demonstrations, you will set up a data engineering workspace, work with the command line, organize project files, and manage code using Git and GitHub. You will then work with CSV, JSON, and Parquet datasets and explore concepts such as schemas, data contracts, schema drift, storage choices, relational databases, and data modeling. Using PostgreSQL and SQL, you will create tables, explore data, perform joins, calculate metrics, and use Common Table Expressions and window functions for more advanced analysis. You will also apply defensive SQL practices for handling NULL values, data types, and inconsistent records. Finally, you will build reliable data workflows by loading file-based data into PostgreSQL, validating data quality, cleaning and preparing datasets, and applying principles such as idempotency, incremental loading, safe reprocessing, and troubleshooting. The course concludes with a structured data processing project that brings together ingestion, storage, transformation, validation, and workflow reliability. By the end of this course, you will be able to: - Explain the role of data engineering in modern data systems. - Describe the stages of the data engineering lifecycle. - Differentiate between batch and real-time processing. - Set up and manage a reproducible data engineering workspace. - Work with CSV, JSON, and Parquet data formats. - Understand schemas, data contracts, schema drift, and storage choices. - Design and query relational data using PostgreSQL and SQL. - Use joins, aggregations, CTEs, and window functions for data analysis. - Apply defensive SQL techniques for NULL values and data types. - Build reliable file-to-database workflows. - Validate, clean, and prepare data for downstream analysis. - Apply idempotency, incremental loading, safe reprocessing, and troubleshooting practices. Designed for aspiring data engineers, data analysts, software developers, database professionals, and students entering the data field, this course prepares you to build structured, reliable, and maintainable data workflows using widely used open-source technologies.