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The course covers topics such as normalization, denormalization, and advanced techniques for modeling complex relationships, temporal data, and schema evolution.
Difficulty
Hours per week
Live Sessions
Next Date
Objective
The objetive is to provide participants with a comprehensive understanding of data modeling principles and techniques in the context of data engineering. The course aims to equip participants with the knowledge and skills to design efficient, scalable, and optimized database schemas for data storage and retrieval. By the end of the course, participants should be able to create well-designed data models that meet business requirements and support data engineering workflows effectively.
Delve into data modeling in data engineering, covering relational, dimensional, and NoSQL types. Understand key concepts like ER modeling, entities, attributes, relationships, and constraints. Explore data modeling tools and notations like Lucidchart, ERDs, Crow’s Foot Notation, and ETV.
Master Entity-Relationship (ER) modeling fundamentals, create entity types, attributes, and relationships. Explore different types of Relational Database Management Systems (RDBMS) with a focus on PostgreSQL.
Learn dimensional modeling for data warehousing using Databricks. Study star and snowflake schema design principles, fact and dimension tables, hierarchies, and attributes.
Explore NoSQL databases and their data modeling approaches. Understand NoSQL principles such as document-based, key-value, columnar, and graph data models. Work with unstructured and semi-structured data.
Comprehend normalization principles (1NF to 3NF) for data structuring. Explore denormalization techniques for performance optimization and learn about trade-offs between normalization and denormalization.
Handle complex relationships like one-to-one, one-to-many, and many-to-many. Study subtyping and inheritance in data models and understand modeling temporal data and effective dating.
Integrate data models into data engineering pipelines. Employ data modeling in ETL (Extract, Transform, Load) processes and understand schema evolution and versioning.
Implement best practices for designing efficient and maintainable data models. Analyze real-world case studies showcasing data modeling in data engineering. Stay updated on emerging trends and advancements in data modeling and tools.
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