Here's how you can optimize data modeling and schema design in data warehousing using logical reasoning.

"Deixe para pensar, quando tiver que pensar", eu não sou o maior fã de reuniões e muitas delas poderiam ser um e-mail, mas dedicar o esforço mental/lógico para definir as metas, requisitos e qual melhor caminho a tomar pode evitar horas de desenvolvimento, refatoramento, entregas erradas e fora do prazo. Ninguém quer gastar recursos para manter dados sem valor e ambientes com modelagem ruim que não funcionam.

Following your goals, use dimensional modeling. It separates factual data (measures) into a central table, surrounded by smaller tables with descriptive details (dimensions). This structure offers several benefits: Reduced redundancy: Dimensions store data efficiently, minimizing waste. Faster queries: Separating analysis-ready data from details speeds up queries. Flexibility: The modular design allows easy addition of new dimensions. Logical reasoning helps you optimize the level of detail in each dimension, balancing efficiency for current needs with potential future requirements.

Iterate on the design, implementation, and operation of the data warehouse to address evolving business needs and technological advancements.

One thing I found useful is to understand the purpose of data warehouse and use it to define the specific data entities and attributes needed for analysis.

An architect needs a clear blueprint of what the building should achieve; likewise, defining business goals gives data modeling a strong foundation for alignment.

Goal of data warehouse architecture is to enable the organization for decision making by using facts and figures. Data modelling helps the developers, Architects and other stakeholders to understand the relationship among different entities in DWH. Goals of data modelling and schema design in DWH is minimize inconsistency, integration of data from multiple sources seamlessly, reduce redundancy, data retrieval and sharing becomes easier and improve quality of data sets for better decision making process in real-time. It should also support data analysis in near real time and encourages collaboration.

Following conceptualization, meticulously define data attributes and analyze data sources to guarantee consistency. Scrutinize for data redundancies (e.g., customer address stored in multiple tables) and normalize the data to establish an efficient and streamlined schema.

To optimize your data model, start with a detailed Entity-Relationship (ER) diagram, focusing on core business entities like customers and products. Define both direct and indirect relationships to ensure comprehensive data connectivity. Employ advanced techniques such as supertype and subtype hierarchies to manage inherited attributes efficiently, reducing redundancy and boosting query performance. Design with future scalability in mind, preparing for increased data volumes or new data types. Continuously refine your ER diagram based on stakeholder feedback and evolving data use patterns. This approach not only enhances current functionality but also ensures long-term adaptability and efficiency.

sualizing data as interconnected rooms in a house helps identify key entities and relationships that form the conceptual model's framework.

– It is the foundation of very data model which represent all business entities and relationship between them. Its main purpose to provide holistic view to business stakeholders of the database.

Maintain clear documentation of table structures, relationships, and indexing strategies to support ongoing development, maintenance, and optimization efforts.

Normalize to safeguard data integrity, but selectively denormalize tables frequently accessed for analytics. This sweet spot optimizes query performance without compromising data reliability.

Just as a house has separate rooms for different functions, normalization structures data into distinct yet connected tables to reduce redundancy and improve integrity.

Relate dimensions: Define hierarchies (e.g., category -> department) for multi-level analysis. Denormalize selectively: Add frequently joined dimension attributes to the fact table for faster queries, but manage storage trade-offs. Ensure consistency: Enforce data integrity rules (e.g., primary/foreign keys) to prevent errors.

Implement appropriate techniques for managing slowly changing dimensions, such as Type 1 (overwrite), Type 2 (historical tracking), or Type 3 (partial historical tracking), to preserve historical data integrity and accuracy.

Dimensional modeling, with fact and dimension tables, is like having a master bedroom (facts) with adjoining closets (dimensions) for efficient data analysis.

Consider future use cases when designing schemas. Logical reasoning allows you to anticipate evolving data volumes and query patterns. Standardize naming conventions and data types for future flexibility. Additionally, leverage data profiling tools to understand data distribution and identify potential skews that could impact query performance. By proactively considering future demands, you can build a data warehouse schema that scales efficiently and adapts to changing business needs.

Schema optimization fine-tunes the data warehouse's layout, like rearranging furniture or adding indexes/partitions to improve query performance and movement.

Verify data integrity by comparing data loaded into the data warehouse against the source systems or original data sets. Evaluate query performance across different types of queries, data volumes, and concurrency levels to identify potential bottlenecks and optimization opportunities.

An architect tests a building's integrity by simulating various conditions; similarly, rigorous testing validates the data model under all scenarios for reliability.

Iterative improvement, incorporating new requirements or technologies, ensures the data warehouse remains a solid, future-proof foundation for evolving business needs.