data-engineering-study-material

data-engineering-study-material is a study guide and reference skill covering data engineering concepts, tools, and best practices. It solves the learning-and-reference problem of having scattered notes by centralizing core principles, modern data stack components, and role-preparation material in one place. It is a documentation/reference resource rather than an installable tool.

The material spans data engineering fundamentals and architecture patterns, ETL/ELT pipeline design, data warehousing and data lake architectures, streaming and batch processing frameworks, cloud data platforms (AWS, GCP, Azure), data quality/governance/observability, infrastructure-as-code and orchestration, and interview preparation. The SKILL embeds concrete Python examples, including a class-based ETL pipeline using pandas and SQLAlchemy, data-quality checks with Great Expectations, and an Apache Airflow DAG demonstrating extract/transform tasks with S3 and Redshift operators. The repository is organized by topic folders (fundamentals, tools, architectures, pipelines, cloud-platforms, streaming, batch-processing, data-quality, orchestration, interview-prep, projects).

It targets aspiring and practicing data engineers, students, and candidates preparing for interviews who want a structured overview of the field with runnable code patterns. Usage is simply cloning the repository and reading the materials; the illustrative code (for example a Postgres connection string with placeholder credentials) is educational reference, not a deployable service.