Data Engineering Solutions

What You Will Learn

• 1 Learn the foundational concepts of data engineering, including the role of a data engineer, core data types, and how modern data ecosystems are structured.
• 2 Understand relational and non-relational database systems, when to use each, and how to design schemas that support downstream analytics and applications.
• 3 Apply data modeling techniques including dimensional modeling, star and snowflake schemas, and entity-relationship design to build analytics-ready data structures.
• 4 Explore batch and streaming data ingestion patterns, tools, and architectural decisions that govern how data moves from source systems into centralized storage.
• 5 Build and manage ETL and ELT pipelines using industry-standard tools, transformation logic, and pipeline testing strategies for reliable data delivery.
• 6 Discover cloud-based data platforms, their core services, and how to architect scalable, cost-efficient solutions using cloud-native storage and compute offerings.
• 7 Implement data warehouse design principles, including partitioning, indexing, and optimization strategies that improve query performance at scale.
• 8 Analyze data quality frameworks, implement validation rules, and develop monitoring systems that detect and alert on data anomalies in production pipelines.
• 9 Understand workflow orchestration concepts and apply pipeline scheduling, dependency management, and failure recovery strategies using modern orchestration tools.
• 10 Develop streaming data architectures using event-driven design, message brokers, and real-time processing frameworks to handle high-velocity data workloads.
• 11 Apply data governance principles including metadata management, data lineage tracking, access control, and compliance strategies to enterprise data systems.
• 12 Create a capstone data engineering solution that integrates pipeline design, storage, transformation, orchestration, and governance into a unified production-ready architecture.