Polypheny provides you with a flexible, highly interoperable system that supports multiple data models to meet your data storage and retrieval needs. This guide will introduce you to the different data models available in Polypheny and help you understand when to use each one.
Relational Data Model
The relational data model is a classic model based on tables and relationships between them, structured through primary and foreign keys. It is ideal for scenarios where data integrity and structured relationships are important. In this model, the data is structured in rows and columns, forming tables. Each row in a table represents a record and each column represents an attribute of the data.
When to use:
The relational model is best suited for applications that require complex queries, transactions, and routine analytics.
Document Data Model
The document model, also known as the NoSQL model, stores data in a document-oriented manner. These documents are independent units which make performance better (related information is read contiguously off disk) and make it easier to distribute data across multiple servers while preserving its locality. Documents are often nested structures, and can thus store related information together for better locality of reference, instead of spreading related data across multiple tables as in a relational model.
When to use:
The document model is ideal for applications dealing with large volumes of data or semi-structured data, which may not fit well into the table structure of the relational model.
Labeled Property Graph (LPG) Data Model
The LPG data model is a type of NoSQL database model that uses graph structures with nodes, edges, and properties to represent and store data. Nodes represent entities or instances such as people, businesses, accounts, or any other item to be tracked. They are connected by directed, named edges that denote relationships between the nodes. Both nodes and edges can have properties associated with them.
When to use:
The LPG model is particularly useful for analyzing the relationships between different entities. It’s perfect for social networking, recommendation engines, fraud detection, network & IT operations, real-time recommendations and more.
Remember, each of these data models in Polypheny comes with its own unique strengths and is designed to solve specific types of problems. Choose the one that fits your application and data structure best.
