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How can you model data warehouses for maximum efficiency?

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Data warehouses are centralized repositories of integrated data from multiple sources that support analytical and reporting applications. To design and build data warehouses that meet the needs of users and perform efficiently, you need to apply some principles and techniques of data warehouse modeling. In this article, we will explore some of the key aspects of data warehouse modeling, such as the types of models, the levels of granularity, the dimensions and facts, and the schemas and architectures.

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Claudio Bonel, M.Sc.

Professor | Pesquisador | Cientista de dados | Engenheiro de Machine learning | Inteligência artificial | Business…
Victor Bertoldo

Data Architect | DBT Certified | Analytics Engineer | Data engineer | Business Intelligence | Data Warehousing | Data…

1 Types of models

Data warehouse modeling is the process of creating a conceptual, logical, and physical design of the data warehouse structure and content. There are different types of models that serve different purposes and audiences. A conceptual model is a high-level representation of the business requirements and the main entities and relationships of the data warehouse. A logical model is a detailed specification of the data elements, attributes, keys, and constraints of the data warehouse. A physical model is a description of how the data warehouse is implemented in a specific database system, including the tables, columns, indexes, partitions, and views.

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Claudio Bonel, M.Sc.

Professor | Pesquisador | Cientista de dados | Engenheiro de Machine learning | Inteligência artificial | Business Intelligence | Python | SQL | Power BI | CSD | PhD Student
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é complicado escolher um "tipo de modelo" ou uma "técnica específica" se vc n tem a vis?o geral, o escopo, os requisitos de negócio. Só a partir deles que você será capaz de projetar uma arquitetura de solu??o/dados e, aí sim escolher a melhor abordagem, a melhor técnica. Essa ansiedade de sair metendo a m?o na tecnologia tem derrubado muitos sistemas de apoio à decis?o (pelo menos na minha experiência). Faz o feij?o com arroz! Engenharia de Software, Engenharia de Requisitos, Sistemas de Apoio à Decis?o, Modelagem de dados. Estuda isso aqui! Beleza? Aí fica mais assertivo no projeto de sua arquitetura de solu??o. Vai ter Data Lake? Só DW? Data Mart? DW consome do Data lake? Tem Golden Record? Vai utilizar parti??o? Fato agregada? Tmj

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Waleed Rehan

Data Engineer | Business Intelligence | Data Analytics | Data Analyst
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First thing first. Modeling approach lays your foundation. To do it efficiently, first understand business requirements, identifying key metrics and stakeholder needs. Choose the right architecture: traditional EDW for structured data, Data Lakes for unstructured data, or hybrid approaches. Use appropriate data modeling techniques: Star Schema for simplicity, Snowflake Schema for normalized structures, or Galaxy Schema for complex environments. Optimize ETL processes for efficient data extraction, transformation, and loading. Implement indexing and partitioning for faster query responses, and regularly monitor and maintain performance. Remember, scalability and security are crucial for long-term efficiency.

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2 Levels of granularity

The level of granularity refers to the amount of detail or aggregation of the data in the data warehouse. The level of granularity affects the size, performance, and usability of the data warehouse. A higher level of granularity means more detailed and atomic data, which allows for more flexibility and accuracy in analysis, but also requires more storage space and processing time. A lower level of granularity means more aggregated and summarized data, which reduces the storage and processing requirements, but also limits the analytical options and quality. The level of granularity should be determined by the business needs and the trade-offs between cost and benefit.

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Victor Bertoldo

Data Architect | DBT Certified | Analytics Engineer | Data engineer | Business Intelligence | Data Warehousing | Data Modeler
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That's THE pain in the ... of any data modeler. When the model is idealized and designed, it follows the scope of what was asked and analyzed. But when things change and the level of granularity no longer matches the goal, there`s a lot of work involved and people have to learn to be a team when that happens. Maybe the stakeholders think that adding a field to the data model is a trivial thing, some times they are right, some times they are not.

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3 Dimensions and facts

Dimensions and facts are the core components of the data warehouse model. Dimensions are the descriptive attributes that define the context and perspective of the analysis, such as time, location, customer, product, or channel. Facts are the quantitative measures that reflect the performance or outcome of the business processes, such as sales, revenue, profit, or costs. Dimensions and facts are organized in tables that follow certain rules and conventions. Dimension tables store the attributes and hierarchies of the dimensions, while fact tables store the facts and the foreign keys that link them to the dimension tables.

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4 Schemas and architectures

Schemas and architectures are the ways of organizing and structuring the dimension and fact tables in the data warehouse. There are different types of schemas and architectures that have different advantages and disadvantages. The most common types of schemas are star, snowflake, and galaxy. A star schema has a single fact table surrounded by multiple dimension tables, which simplifies the queries and improves the performance. A snowflake schema normalizes the dimension tables into multiple levels, which reduces the redundancy and improves the consistency. A galaxy schema has multiple fact tables that share some dimension tables, which allows for more complex and diverse analysis. The most common types of architectures are enterprise data warehouse (EDW), data mart, and data lake. An EDW is a centralized and comprehensive data warehouse that serves the entire organization and integrates data from multiple sources. A data mart is a smaller and focused data warehouse that serves a specific department or function and extracts data from the EDW or other sources. A data lake is a large and flexible data repository that stores raw and unstructured data from various sources and enables ad-hoc and exploratory analysis.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Claudio Bonel, M.Sc.

Professor | Pesquisador | Cientista de dados | Engenheiro de Machine learning | Inteligência artificial | Business Intelligence | Python | SQL | Power BI | CSD | PhD Student
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Creio que nesse artigo, o primeiro tópico deveria estar associado a compreens?o do requisitos de negócio (funcionais e n?o funcionais), para que em seguida você: 1 - Projete sua arquitetura de solu??o 2 - Projeto sua arquitetura de dados (Inmon? Kimball? Outro modelo?) 3 - Desenvolver um modelo conceitual (MER), a partir da compreens?o dos requisitos ou, como chamamos, minimundo. 4 - Desenvolver o seu modelo lógico, garantindo aderência ao negócio, desempenho, seguran?a, confiabilidade (disponibilidade e escalabilidade) e integridade 5 - Implementar o modelo lógico Naturalmente, precisa estudar modelagem de dados (relacional e dimensional), bem como suas técnicas. Dúvidas? Chamaí!

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How can you model data warehouses for maximum efficiency?

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5

1 Types of models

2 Levels of granularity

3 Dimensions and facts

4 Schemas and architectures

5 Here’s what else to consider

Data Warehousing

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How can you model data warehouses for maximum efficiency?

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1 Types of models

2 Levels of granularity

3 Dimensions and facts

4 Schemas and architectures

5 Here’s what else to consider

Data Warehousing

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