Data Model Design — Data Engineering High Performance Design Patterns (Series 3/11)
Prerequisite: You can read the introduction here in this post…
Chapter 3: Data Model Design
In this crucial chapter, we dive into the intricacies of Data Model Design, a cornerstone in the architecture of high-performance data engineering systems. This chapter focuses on developing a robust foundation through comprehensive logical and physical data models, enriched with metadata for effective data integration.a
1. Comprehensive Data Models
1.1 Logical Data Model Development
We explore the development of a logical data model that serves as the conceptual blueprint, offering a high-level representation of data entities, their relationships, and the flow of information. By incorporating metadata, we empower ETL developers and Quality Assurance teams to gain a nuanced understanding of the data and its interconnections.
1.2 Physical Data Model Development
Delve into the tangible aspects of data modeling with the creation of a physical data model. This step involves translating the logical model into a structure that aligns with the chosen database technology, ensuring efficiency in storage and retrieval.
1.3 Metadata Integration
Understand the critical role metadata plays in the success of data integration. Learn to leverage metadata to facilitate a deeper understanding of data elements, lineage, and transformations, enhancing the overall efficiency of the system.
2. 3NF Data Model for Data Warehouse Databases
2.1 Choosing a 3NF Data Model
Explore the rationale behind selecting a Third Normal Form (3NF) data model, especially tailored for Massively Parallel Processing (MPP) databases like Bigquery, Redshift, and Teradata etc. Understand how this normalization contributes to a high-performance data integration layer.
2.2 Reducing Redundancy and Improving Processes
Learn techniques to eliminate redundant data within the data model. By adopting normalization strategies, discover how to streamline data load processes, optimizing the overall efficiency of the data integration system.
3. Key Strategies for Dependency Management
3.1 Natural/Business Keys vs. Surrogate Keys
Navigate the decision-making process between natural/business keys and surrogate keys. Understand the implications of each and how to minimize dependencies to enable more flexible data reloading.
3.2 Defining Foreign Keys for Metadata
Discover the importance of foreign keys in enhancing metadata within the data model. Learn to define foreign keys strategically, without enforcing them at the database level, ensuring a balance between metadata richness and system performance.
Conclusion
By the end of Chapter 3, participants will have a profound understanding of the intricacies involved in crafting a data model that not only aligns with the principles of high-performance design but also sets the stage for efficient and scalable data engineering systems. The emphasis on normalization, metadata utilization, and key management strategies equips practitioners with the tools to architect a robust data model for their specific use cases.
