Graph Database
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Elevate Challenges in Enterprise Digital Transformation

Data Difficult to Find-galaxybase-为什么是图数据库
Data Difficult to Find
A vast amount of historical data accumulated and scattered across various systems, making it difficult to quickly locate the required data.
Data Management Challenges-galaxybase-为什么是图数据库
Data Management Challenges
Difficulty in integrating data silos, high costs associated with building data lakes and data warehouses, and a lack of effective data governance mechanisms.
Data Difficult to Use-galaxybase-为什么是图数据库
Data Difficult to Use
Lack of a unified business data perspective and views, making it challenging to address specific business issues using the data.
Graph databases, driving business innovation through connected data, emerge as a new choice for digital transformation.

What is a graph database?

A graph is a data structure used to describe network relationships between entities in the real world. A graph database, on the other hand, is an online data management system that operates semantically based on graph structures. It utilizes vertices (vertex) and edges (edge) to represent and store data, supporting operations for adding, deleting, modifying, and querying data.

Unlike traditional relational databases (RDBMS), graph databases store relationships directly. When performing relational queries, they do not require expensive and time-consuming JOIN operations with foreign keys as in relational databases. Compared to the traditional table structure storage model, this storage method in graph databases is more natural, focusing on the relationships between objects, providing an intuitive representation of the real world.

As the diagram below illustrates using Galaxybase graph database, it represents the relational connections between various entities such as directors, movies, actors, movie genres, and more.


From a storage perspective, traditional relational databases store and manage data in a "two-dimensional table" format composed of rows and columns. In contrast, graph databases use nodes and edges as the fundamental storage units, designed for efficient storage and querying of graph data. Graph databases and relational databases share similarities with online transactional processing (OLTP) databases in supporting data manipulation operations (create, read, update, delete), typically accessed directly by applications.

Graph Database Classification
Based on the underlying storage types, graph databases are categorized into native and non-native graph databases.

Native Graph Storage

Does not rely on third-party relational or NoSQL storage systems. Data is stored directly in a graph structure at the underlying level, maintaining consistency between the storage and processing layers in terms of data model. This approach facilitates seamless interaction between storage and computational systems.
Suitable for: Scenarios with large data scales and frequent deep link queries, particularly well-suited for the current era of big data development.
Native Graph Storage

Completely Non-Native

In this approach, the data layer utilizes relational databases, key-value databases, document databases, or other multimodal databases for storage. The processing layer achieves relationship queries through multiple table joins and field indexing, only presenting data in a graph format at the business layer.
Suitable for: Scenarios with small data volumes, few relationship hops, simple table relationships, and static data.
Completely Non-Native

Non-Native Storage

In this setup, the data layer employs non-native storage structures like key-value stores. The processing layer approximates adjacency without indexing. Since the storage layer lacks complete graph semantic support, there is an additional overhead of converting from a non-graph to a graph model between the storage and processing layers during graph queries and computations, leading to performance degradation.
Suitable for: Scenarios with a small number of nodes and edges in queries, and shallow relationship links.
Non-Native Storage
Why Choose a Graph Database?
In the real world, every person, event, object, and location is interconnected. These relational ties naturally exist within the data that describes the objective world and play a crucial role in business decision-making.
As data transitions from being a "supporting asset" to a "critical strategic asset" in enterprise operations, the competitive landscape of the digital age demands that companies elevate their ability from "effectively managing data" to "efficiently extracting data value."
Advantages of graph databases compared to relational databases:
More intuitive and flexible model:
The graph data model directly reflects business scenarios, making it more intuitive and flexible compared to traditional data models. It can effectively handle dynamically changing data relationships, enhancing communication efficiency between product and development teams, and reducing the costs associated with operating and modifying data models.
Simpler query language:
Graph databases offer a more concise query language, such as the widely used Cypher graph query language. Compared to SQL, Cypher significantly reduces the code volume for complex relationship queries, enhancing application development efficiency.
Simpler query language:
Graph databases offer a more concise query language, such as the widely used Cypher graph query language. Compared to SQL, Cypher significantly reduces the code volume for complex relationship queries, enhancing application development efficiency.
Development of the Graph Database Market
Gartner《2022 年图数据库管理系统市场指南》
According to Gartner's "2022 Market Guide for Graph Database Management Systems," the graph technology market, including graph database management systems (DBMSs), is projected to grow to $32 billion by 2025, with a compound annual growth rate of 28.1%.
IDC《IDC MarketScape: 中国图数据库市场 2023 年厂商评估 》
IDC's "IDC MarketScape: China Graph Database Market Vendor Assessment 2023" analyzes that "China's graph database market reached RMB 240 million in 2022, and it is expected that the overall market size will be more than RMB 400 million in 2023, showing rapid growth.

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