GK840040 Data Architecture

COURSE OBJECTIVE:
• Understand the principles and responsibilities of Data Architecture. • Design efficient data models and Entity-Relationship Diagrams (ERDs). • Solve real-world data modeling challenges and optimize schemas. • Compare and integrate structured, unstructured, and hybrid data solutions. • Architect cloud-native and hybrid systems, integrating ETL/ELT pipelines. • Build real-time processing systems with tools like Kafka. • Apply best practices in data governance, quality, and security. • Design scalable architectures for AI/ML workflows. • Create end-to-end data architectures for business use cases.

TARGET AUDIENCE:
This course is ideal for Data Engineers, Database Administrators, Big Data Specialists, Data Analysts and Scientists, and Cloud Architects who are looking to enhance their skills in data architecture and management.

COURSE PREREQUISITES:
• Familiarity with database systems, SQL, and basic data management concepts. • Working knowledge of at least one programming language (e.g., Python, Java, or Scala). • Basic understanding of data engineering or data analysis workflows. • Awareness of ETL processes and data integration principles. • Familiarity with cloud platforms (e.g., AWS, Azure, or Google Cloud) or interest in transitioning to cloud-based systems. • Awareness of big data concepts and tools (e.g., Hadoop, Spark) but no hands-on experience required. • Understanding of basic data modeling techniques (e.g., star schema, snowflake schema). • Awareness of data governance and security concepts. • Familiarity with modern data architectures, such as data lakes or warehouses.

COURSE CONTENT:
• Data Architecture Fundamentals • Introduction to Data Architecture • Data Modeling Concepts • Relational vs Modern Data Warehouses • NoSQL Databases • Data Lakes and Delta Lakes • Data Design Concepts • Cloud Data Architectures • OLTP vs. OLAP • Lambda and Kappa Architectures • ETL vs. ELT • Data Pipelines for AI/ML • AI, Data Governance, Security, and Management • Scalable Data Architectures for AI/ML • Deployment and Optimization of AI/ML Systems • Data Governance and Quality • Data Security Best Practices

FOLLOW ON COURSES:
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