Master Data Engineering: Concepts to Production

Data Engineering: SQL, Python, Unix, Spark, Cloud, AWS, ETL, Data Quality , Data Governance & Data Architecture

Master Data Engineering: Concepts to Production is a comprehensive course designed to transform beginners into proficient data engineers. Starting with foundational concepts (data lifecycle, roles, and tools), the course progresses to hands on skills in SQL, ETL processes, UNIX scripting, and Python programming for automation and data manipulation. Dive into big data ecosystems with Hadoop and Spark, learning distributed processing and real-time analytics. Master data modeling (star and snowflake schemas) and architecture design for scalable systems.

Explore cloud technologies (AWS) to deploy storage, compute, and server less solutions. Build robust data pipelines  and orchestrate workflows, while integrating CI CD practices for automated testing and deployment. Tackle data quality methods (validation, cleansing) and data governance principles (compliance, metadata management) to ensure reliability.

Each chapter combines theory with real world projects: designing ETL workflows, optimizing Spark jobs, and deploying cloud-based pipelines. By the end, you’ll confidently handle end to end data solutions, from raw data ingestion to production ready systems. Ideal for aspiring data engineers, analysts, or IT professionals seeking to up skill.

Prerequisites: Basic programming knowledge.

Tools covered: Spark, Hadoop, AWS, SQL, Python, UNIX, Git, IntelliJ IDE.

Outcome: Build a portfolio of projects showcasing your ability to solve complex data challenges.