Modern applications function on data. However, as these applications advance, the data they run on also grows in size and complexity. Data and systems are co-dependent; applications need data to operate, and data needs enough storage to avoid obsoleting. Earlier, data had specific functions. Today, however, applications need robust, instant, and versatile data that traditional datasets don’t provide.
A graph database (GDB) solves this challenge by making data processing faster and more efficient.
Introduction to Graph Databases
What is a Graph Database?
A GDB or NoSQL, as it is commonly called, is a modern-day database that stores data using graph structures. Unlike traditional databases that store data in tabular forms or documents, graph databases use nodes, relationships, properties, and labels. This advanced data structure system accelerates data retrieval in a single operation. Based on graph theory, graph databases keep data in the graph’s nodes.
Understanding the Differences between Graph Databases and Traditional Relational Databases
Graph database vs relational database compares their relationship with the data they store. In graph databases, relationships are structured individually; their levels are recorded. On the other hand, relational databases operate on traditional structures, such as tables and documents.
Furthermore, relational databases handle large record numbers quicker, as they rely on predefined and pre-set data structures. In contrast, graph databases’ lack of a prescribed format makes them a better option because it allows them to monitor each record individually to identify the data structure.
Graphing Database Model
Understanding the components of graph databases is crucial to incorporate them correctly. Graph databases have four core elements: nodes, relationships, labels, and properties.
- Nodes: Nodes are a graph’s focal point, similar to rows and columns in traditional database tables.
- Relationships: The connection between the nodes refers to their relationship.
- Labels: Similar nodes are grouped under the same category, known as their labels.
- Properties: Properties refer to the critical pairs kept within two or more nodes.
Relational Database Model
Relational databases need a predefined set of carefully structured tables. Each table is modified with the required attributes, known as columns. However, unlike a graph database, relational databases lack flexibility; they are rigid and provide limited features.
The Benefits of Using Graph Databases for Data Science and Analysis
Using a graph database in data science makes data science and analysis more efficient and effective. Graph databases result in better performance, agility, and flexibility when used correctly.
Why Use Graph Databases?
Better Performance
Sometimes, using relational databases makes it challenging to compute and handle data. In traditional databases, as the magnitude and layers of relationships increase, relationship complaints meet a dead-end. On the flip side, graph databases improve data handling performance by a significant margin by remaining constant, despite the data size and space.
Enhanced Agility
Modern-day systems need constant upgradation to meet the evolving market trends, needs, and technological development. Companies need a solid data processing system to satisfy their current and future business goals. A graph system’s ability to mould itself according to present-day development requirements enables efficient data management and automatically maintains and updates data systems.
Increased Flexibility
Instead of working individually, graph databases make it easier for different technological departments, such as IT and data engineering teams, to collaborate seamlessly. Since graph databases’ components accelerate performance, they can modify the current graph system without compromising its structure, altering the data, or remodelling it constantly.
How to Effectively Use Graph Databases in Data Science Projects
Today, organizations use a graph database in data science in various ways to make their operations smoother and easier to accomplish.
Graph Database Use Cases
Fraud Detection
Graph databases play a significant role in preventing fraud by detecting fraud rings. Using graph databases enables subtle data monitoring by presenting real-time fraud analytics. When a relational database generates fraud reports, the perpetrators have already caused damage. Graph databases prevent this scenario by performing relationship-based actions in real-time, allowing fraud detection departments to stay ahead of the criminals.
Manufacturing Industry
The manufacturing industry relies on networking to obtain and process resources. Since graph databases find information easily and quickly, manufacturers can instantly extract information about their vendors and dependencies.
Government Operations
Another way graph database in data science transforms the current landscape is by helping government operations. Graph technologies solve various government problems, such as eliminating embezzlement, preventing tax fraud, assisting in criminal investigation, and aiding in tracing contacts.
Data Laws
Data is a company’s most valuable asset. In addition to storing sensitive and confidential information, organizations acquire, process, and sell it to conduct the most basic business functions. However, data regulations constantly evolve to keep the increasing data size in check, making it challenging for companies to adhere to regulation policies and privacy guidelines. Graph databases make data structuring more efficient, allowing companies to store and track it per their needs and requirements.
Case Studies of Successful Data Science Projects that have Utilized Graph Databases
There are endless graph database examples in the data science landscape. Graphing technology has equipped the data science industry with a robust data computation tool, generating many success stories. The two most famous case studies are Walmart’s and Pitney-Bowes’s experience with graph databases.
Walmart
Walmart is a family-owned business that earned its name as the world’s biggest retailer in five decades, employing over two million people. The large corporation believes in maintaining a mutually beneficial relationship with its customers, which it achieved using the Neo4j database.
Since Walmart interacts with over 250 million customers weekly, its Brazilian office uses graph databases to understand its online customers’ buying patterns. Understanding user behaviour allows Walmart to make personalized and real-time changes to the user experience and increase the revenue stream.
Pitney-Bowes
Pitney-Bowes, a direct and digital marketing leader, wanted to assist its clients by creating an omnichannel experience filled with relevant information. Using a graphing database, Pitney-Bowes implemented a scalable infrastructure for relationship-related queries in an agile and cost-effective manner.
The Future of Graph Databases in Data Science and Their Impact on the Industry
Data leaders encounter challenges when managing large datasets and obtaining relevant information from them. When computing data, it is essential to establish a connection between data points to make dataset analysis more effective.
Traditional or relational database management systems store structured data, but their functions are rigid. Given their predefined features, capturing and keeping data becomes difficult while establishing connections between data points. On the other hand, a graphing database understands complex data by studying the relationships between nodes, allowing companies to analyze data effectively.
Graph databases keep query-connected data via nodes, making them the perfect tool to solve challenges during critical operations. Furthermore, graphic systems visually depict data and the relationship between different datasets, enabling data scientists to study the data content, monitor patterns, and predict changes or threats.
Final Thoughts
A graph database makes all data science-related functions accessible, secure, and faster. Graph databases use nodes, labels, and relationships to obtain and process relevant data and store it safely to prevent it from becoming obsolete.
