Master Data Engineering Concepts to Production

Master scalable data pipelines, cloud architectures, and enterprise-grade systems from theory to deployment
What you'll learn
Hands on Python, SQL, Unix, Hadoop, Spark, CICD, ETL using IDE to replicate real life data engineering workflow
Design, build, and manage scalable data pipelines using tools like Spark and frameworks for job orchestration, ensuring efficient data flow from ingestion to co
Model data warehouses/lakes using star/snowflake schemas and optimize storage for analytics.
Enforce data governance with quality checks, metadata management, and compliance frameworks
Master advanced SQL for complex queries, ETL transformations, and database optimization.
Troubleshoot pipelines using logging, monitoring tools, and error-handling strategies.
Leverage cloud tools (AWS EC2, S3,Lambda) for cost-effective, auto-scaling data workflows.
Identify real world problem statement, design and implement data pipeline.
Requirements
Basic Programming Knowledge
No Prior Data Engineering Experience Needed
Access to a Computer & Internet
Curiosity about data workflows, databases, or cloud tools.
Description
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.
Who this course is for
Beginners with basic programming skills aiming to enter the field.
Professionals seeking to transition into engineering roles (ETL, pipelines, automation).
Developers or sysadmins wanting to specialize in scalable data systems, cloud (AWS), and big data tools.
Individuals with coding fundamentals pivoting to data engineering.
Teams needing modern data skills (Spark, Hadoop, CI/CD, governance) for enterprise projects.
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