Building Efficient Data Models: A Guide to Doris Database Data Modeling Design

# 1. Fundamentals of Data Modeling**

Data models are abstract representations of data organization and storage, defining data structures, the relationships between data elements, and rules for data operations. A good data model can enhance the efficiency of data queries and analyses and provide a reliable foundation for business decision-making.

Data modeling should adhere to certain principles, including performance priority, scalability, and ease of maintenance. The data modeling process generally consists of three phases: requirement analysis, data modeling, and data validation. During requirement analysis, the needs and goals of the data model are determined; data modeling creates the structure and relationships of the data model based on these requirements; and data validation ensures the data model meets requirements through testing and analysis.

# 2. Doris Database Data Modeling Design Principles

### 2.1 Overview of Data Modeling Design Principles

Data modeling design principles guide the data modeling process in the Doris database, ensuring that the data model meets the requirements of performance, scalability, and ease of maintenance.

#### 2.1.1 Performance Priority

Performance is the primary principle in data model design. The data model should be designed to maximize query performance while maintaining data consistency and integrity. This includes:

- Choosing appropriate storage formats and compression algorithms
- Using partitioning and indexing to optimize data access
- Avoiding unnecessary redundancy and complex data structures

#### 2.1.2 Scalability

Data models should be scalable to support growing data volumes and user needs. This includes:

- Using partitioning and sharding to horizontally scale data
- Using replication and backups to ensure data redundancy and availability
- Designing scalable data structures to support future expansion

#### 2.1.3 Ease of Maintenance

Data models should be easy to maintain, allowing for updates and expansions as business needs change. This includes:

- Employing clear and consistent data naming conventions
- Adopting modular design for easy modification and expansion of data models
- Providing tools and documentation to support the management and maintenance of data models

### 2.2 Data Modeling Design Process

The data modeling design process is an iterative process involving the following steps:

#### 2.2.1 Requirement Analysis

The first step in data modeling design is analyzing business requirements. This includes determining the queries, reports, and analyses that the data model should support. Requirement analysis should consider the following factors:

- Data sources and data formats
- Data usage scenarios and query patterns
- Performance and scalability requirements

#### 2.2.2 Data Modeling

After requirement analysis, the next step is to construct the data model. The data model should reflect business entities and relationships and meet the principles of performance, scalability, and ease of maintenance. Data modeling techniques include:

- **Entity-Relationship Diagram (ERD):** Used to visualize data entities and their relationships.
- **Star Schema and Snowflake Schema:** Used to organize multidimensional data.
- **Dimensional Modeling:** Used to organize hierarchical data.

#### 2.2.3 Data Validation

Once the data model is completed, it needs to be validated to ensure it meets the requirements. The validation process includes:

- **Syntax Validation:** Checking if the data model conforms to the Doris database's syntax rules.
- **Logical Validation:** Checking if the data model is logically correct and capable of supporting the expected queries and analyses.
- **Performance Validation:** Running query and analysis benchmarks to evaluate the performance of the data model.

# 3. Doris Database Data Model Types

The Doris database supports various types of data models