A database is one of the most crucial sections of an application. Databases can store information and enhance the overall performance of the software. That is why choosing an appropriate database for your project is critical.
Today, the popularity of graph databases is significantly soaring. The graph database market size is estimated to jump from $821.8 million in 2018 to $2.5 billion by 2023. More and more companies have understood that the real power is not in the data itself but in how the data are connected.
Many applications work with relational databases such as MySQL and PostgreSQL. Despite their benefits, relational databases can hardly handle innumerable amounts of connected data. That is why non-relational databases like Neo4j are optimized to help you build high-performance and scalable applications that easily use countless amounts of connected data. Not many developers know about the capabilities of graph databases and Neo4j. In this article, we are going to explain everything about Neo4j and its capabilities.
Concepts and principles of Neo4j databases
Before thoroughly discussing the role of the Neo4j database in real projects, you need to know about how this technology works, the business purposes you can use it for, and the differences between Neo4j and other databases.
Graph databases: best solution for handling connected data
Everything in the world is connected. Take our inner circle of friends and family, for example. Everyone is related to others in different ways. Imagine that all the data explaining the relations between the circle members are stored in one place. Then, you can take any data without worrying about other connections.
This is how a graph database like Neo4j works. Graph databases are NoSQL databases that can store, map, and query relationships between data. Elements in a graph database can connect to each other in any possible way.
A graph database like Neo4j is the best solution for handling large amounts of connected data. These databases are focused deeply on relationships and store already-connected data, unlike any other data storage and management technologies available. That is why graph databases are the most effective technology to handle countless connected data quickly.
Relational databases and non-relational databases
The developers who have only worked with relational databases throughout their career will certainly have this question in mind, “what exactly is the point of using non-relational models like Neo4j?”
When using relational databases, everything appears to be seamless and clear. Despite that, there are some significant drawbacks in using these databases you need to be aware of:
- Limited Volume: Relational databases are not well optimized to process large amounts of data.
- Speed: Relational stores are not fast when they need to process a huge number of reading and writing operations.
- Lack of Relationships: Relational data stores are limited to describing only standard relationships, including one-to-one, one-to-many, and many-to-many relationships.
- Variety: Relational databases are barely flexible when processing the types of data that cannot be described using the model schema. In addition to that, these databases are inefficient when dealing with big binary and semi-structured data (JSON and XML).
- Scalability: Horizontal scaling is ineffectual for relational databases.
To tackle all of these issues and limitations, different non-relational databases like Neo4j have been developed. However, most of them lack relationships that are due to associating pieces of data with each other through references (similar to foreign keys in the relational model). References make the process of querying data more difficult, especially connected data, as they try hard to describe relationships between entities.
Neo4j Graph Database
Graph databases like Neo4j are mainly based on graph theory, which is a mathematical theory. Graphs are structures that consist of two main parameters: vertices and edges.
Vertices represent entities such as people or things in a large database. Edges also show the connections between vertices. Edges can have numerical values that are called "weight."
Developers can make use of these structures to model scenarios that are defined by relationships. As an example, a simple graph database lets developers model a social network consisting of users as nodes and relationships that are the connections between the users. Another example can be a road network where cities, towns, or villages are vertices, and on the other hand, roads are edges that connect the vertices with weights indicating distances.
Neo4j tends to show the concepts of graph theory in its own way. To find it out exactly, we need to take a look at the Labeled Property Graph Model in the Neo4j database below.
Main components of Neo4j database
The Neo4j model mainly consists of these primary components:
- Nodes (equivalent to vertices in graph theory): Main data elements (e.g., Jack or other members of a friendship circle) that are connected by relationships. Nodes can have labels and properties (explained below).
- Relationships (equivalent to edges in graph theory): Describes the connections between the nodes and connects them together (e.g., Jack is "married to" Jane). Relationships can have one or more properties.
- Labels: Represent the role of nodes (e.g., Jane is a "person.”) Labels are used to group nodes. Each node can have multiple labels. Labels are also indexed to expedite the process of finding nodes in a graph.
- Properties: Attributes of nodes and relationships involving name or value pairs.
Neo4j database allows storing data as key-value pairs, meaning that properties can have any kind of value (string, number, or boolean.) The graph data structure may initially seem a little complicated, but it is simple and natural. Take a look at the picture below as an example of the graph data model in Neo4j for more clarification:
In this simple model, two main nodes are Alice and Bob. They are connected to each other through relationships. Both nodes have a similar label, which is "Person." In this model, only Bob's node has been given some properties; however, in the Neo4j graph model, every node and relationship can contain properties.
It is easy for people to interpret a Neo4j model as it is intuitive and understandable. The truth is that the human brain barely thinks based on tables and rows and tends to think about abstract objects and connections. In other words, anything that you can draw on a piece of paper can be depicted with graphs and turned into a Neo4j model.
Neo4j vs. Relational and other NoSQL databases
Now that we know the basics about the Neo4j database and the graph data model, you may be wondering about the difference between the Neo4j database and relational data stores. Although Neo4j is on the list of NoSQL tools, it is still different from other NoSQL databases. For this purpose, it is essential to know the differences between the Neo4j database and other relational and non-relational databases below.
Data Storage
In the case of data storage, the Neo4j database uses a graph storage structure. Relational databases use fixed, predefined tables consisting of rows and columns. Also, NoSQL databases use connected data storage that is not supported at the database level.
Data Modeling
Neo4j databases make use of a flexible data model, while the model in relational databases must be developed from a logical model. Additionally, NoSQL databases are not suitable for enterprise architectures.
Query Performance
The Neo4j model offers tremendous performance regardless of the number and depth of connections. On the other hand, the processing speed of relational databases decreases as the number of data entries grows. Also, the relationships must be created at the application level in NoSQL databases.
Query Language
Cypher language is used in the Neo4j model, which is the native graph query language. The SQL language is used in relational databases, which increases the complexity as the number of joins grows. In the case of NoSQL models, different languages are used, but none of them is developed well to express relationships.
Transaction Support
ACID transactions are supported in both the Neo4j and relational models. Speaking of NoSQL databases, BASE transactions prove unreliable for data relationships.
Processing At Scale
The Neo4j database is inherently scalable for pattern-based queries. Relational databases scale through replication but are not cost-effective. NoSQL databases are also scalable, but data integrity is not trustworthy in them.
Benefits of Neo4j database
Neo4j models are specifically designed to handle significant amounts of connected data. These models provide you with some main advantages, including the followings:
- Fast Performance
This is one of the biggest advantages of graph models. The performance of relational databases is not sufficient as the number and depth of relationships increase. On the other hand, the performance of graph databases such as the Neo4j databases remains high even if the amount of data increases remarkably.
The team behind the Neo4j model has also released a library recently. The library allows developers to run graph algorithms in parallel on many billion nodes and tens of billions of relationships in a couple of hours. In more exact terms, the Neo4j databases scale horizontally. It means that the model's performance is not dependent on the size of the database. It can traverse enormous sets of connected data and offers enterprise database features such as ACID transactions and automated backup or recovery.
- Flexibility
The structure and schema of a graph model like Neo4j are easily adjustable to the application variations, making them a highly flexible database. You can also easily upgrade the data structure without harming the existing functionality. The upgrade can be done anytime you want, as the structure can evolve simultaneously along with the application it is used for.
- Manage Relationships Between Data
Neo4j database allows you to explore various paths and connections between the data and query them as efficiently as possible. Furthermore, you can fetch intricate data from the database with ease, even if they are strongly connected.
Use cases of Neo4j database
The next thing we need to know about the Neo4j graph model is what we can use this data store technology for. It may seem like this technology can be acquired to solve any kind of problem, but the truth is that the Neo4j database should also be used when it is suitable.
The Neo4j model is used only when connected data matters the most. This technology has already conquered the most popular use cases, including fraud detection, personalization, network management, knowledge graphs, and many more.
Despite that, the next generation of graph developers is engineering the future of artificial intelligence and machine learning with the help of models like Neo4j. Now, let us take a look at several use cases of the Neo4j database below.
Stopping Fraud Rings
Traditional fraud prevention measures concentrate deeply on separate data points, including accounts, individuals, devices, or IP addresses. The problem here is that modern criminals are today able to escape these detection methods simply by creating fraud rings with unreal identities. To prevent such escapes, it is necessary to look at the connections that link the individual data points.
Although no fraud prevention measure is perfect, you can enhance the process by analyzing the connections between individual data. This is where the Neo4j model comes in handy to detect difficult patterns that relational databases can hardly uncover.
Enterprise organizations use the Neo4j database to boost their fraud detection abilities to prevent various financial frauds, including first-party bank fraud, e-commerce fraud, credit card fraud, insurance fraud, and money laundering fraud, immediately.
Network and IT operations
Network and IT infrastructures are extremely complex and require a configuration management database (CMDB) that is far beyond relational databases. The Neo4j CMDB graph database helps you correlate your network, data center, and IT assets to simplify troubleshooting, impact analysis, and capacity or outage planning.
Graph databases like Neo4j empower you to connect monitoring tools and achieve crucial insights into the complicated relationships between various network or data center operations. There are unlimited uses for graphs in network and IT operations.
Recommendation engines
Real-time recommendation engines must be able to correlate product, inventory, customer, supplier, logistics, and even social sentiment data to work most efficiently. Furthermore, they should be able to instantly capture any new interests according to the new customer's visit.
The key technology that enables recommendation engines to do so is graph databases like Neo4j. It quickly leaves traditional relational databases behind and connects massive amounts of customer and product data.
Social media applications
Social graph databases like Neo4j help create innovative social networks or integrate current social graphs into an enterprise application. The truth is that social media networks are already built with graphs and relationships; then, so there is no point in changing them from graphs into tables and then back again.
A data model that can directly match your domain model will help you understand your database, communicate better, and reduce needless work. Neo4j expedites the performance of your social network application by decreasing the time required for data modeling.
Management of Identity
Managing countless roles, groups, products, and authorization at an enterprise is difficult. With Neo4j, you can efficiently track all identity and access authorizations and inheritances deeply and fast. That is because all the data is interconnected in Neo4j, giving you better insights and control than ever.
Telecommunications
Connections are the core of telecommunication, and graph databases like Neo4j are:
- The best choice for modeling.
- Storing.
- Querying all types of telecommunication data.
Neo4j enables businesses to perform fast by improving their connected data, whether they need to manage supercomplicated network structures, various product lines and bundles, or customer satisfaction and retention in today's widely competitive market.
Government
Many governments are currently using Neo4j and other graph technologies to combat crime, prevent terrorism, enhance fiscal responsibility, and make everything transparent to their citizens. These approaches need data to get connected across various applications or repositories, requiring discrete departments to work. That is why a flexible, scalable, and powerful graph model like Neo4j is necessary to quickly process the interconnected data.
AI and analytics
Modern businesses are dealing with extremely complicated challenges today and require intelligent technologies. Neo4j in this case improves predictions that make better decisions and innovation possible. It incorporates the predictive power of relationships and network structure in current data to answer difficult questions and soar prediction accuracy.
The Neo4j graph algorithms find essential patterns in global structures and make predictions about the graph possible with the help of graph embeddings and graph database machine learning training inside of the analytics workspace. That is how businesses can enhance highly predictive relationships and network structure to answer uncommon questions.
Organizations use the results of graph algorithms and predictive features of Neo4j for further analysis, machine learning, or supporting artificial intelligence systems. Graphs generally bring fantastic value to advanced analytics, machine learning, and AI.
Life sciences
Interconnected data massively surround companies working in life sciences. For example, biologists need to understand the connections between genes, proteins, cells, etc., and healthcare organizations need to map patient journeys to understand disease progression. That is why these companies are using Neo4j to analyze their connected data, previously impossible without graphs.
Life companies, including chemical manufacturers, agriculture companies, biotech startups, and many others, understand the real value of Neo4j and graphs for R&D, privacy and regulatory compliance, medical equipment manufacturing, patients, organizations, etc.
Financial services
Financial services and banks must continuously fight financial crimes, prevent and respond to cyber attacks, and ensure they comply with updated regulations. To do so, they require a database that can rapidly find relationships between data points with ease. Neo4j helps financial services and provides them with better risk management, regulatory compliance, reliable and secure IT structure, enhanced customer experience, and many other benefits.
The biggest users of Neo4j in the world
Today, the biggest organizations in the world are using Neo4j to optimize managing their innumerable data points. Neo4j is the world's leading provider of scalable graph technology that helps 75 percent of the companies in the Fortune 100 list enhance their connected data applications.
The biggest industries and companies using the Neo4j technology today are as follows:
- 7 of the world's top 10 retailers like eBay, ADEO, and ATPCO
- 3 of the world's top 5 aircraft manufacturers like Airbus
- 8 of the world's top 10 insurance companies like Bayerische and Allianz
- All of North America's top 20 banks like JP Morgan, Citi, Chase, and UBS
- 8 of the world's top 10 automakers like Volvo, Toyota, and Daimler
- 3 of the world's top 5 hotels like Marriott and AccorHotels
- 7 of the world's top 10 telcos like Verizon, Orange, AT&T, and Comcast
Final words
Modern applications today face the big challenge of processing huge amounts of interconnected data, which is why they are massively in need of efficient technology to help them cope with this issue. Neo4j graph technology allows you to create applications that are able to provide worthwhile, real-time insights into connected data for further analysis and make correct decisions.
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