Graph databases have proven to be a disruptive way for organizations including software development companies to address needs that other methods simply cannot.
Consider the situation where you need to analyze complex networks, find out whether a bank transaction is fraudulent, provide product recommendations, or trace a lost product.
Finding answers to each of these questions can often be complex and time consuming. But if you use a graph database, you can view the data landscape in an entirely different perspective. The possibilities are simply endless.
What is a graph database?
In a very simple way, a graph database is a database that uses graphs instead of tables to store data. This makes them ideal for storing and managing data that is interconnected, such as social media data or the Internet of Things.
The concept of a graph database becomes way easier if we understand what graphs really look like. Try this out, google «graphs» and see what kind of images you get. Most likely, you will get something like the image below.
However, in mathematics, the term graph is used synonymously as a network, implying a graph consists of data entities, also called nodes, connected by strings, which are known as edges. The image below is a true representation of a graph in the context of computing. .
Like the image above, graph technology uses the nodes and edges structure to store, manipulate, update, and access data. Nodes are the key entities in a graph database. They represent data points. Edges are the relationships between nodes and are used to connect data.
Due to this interconnectivity, graph databases pave the way for efficient data usage compared to their traditional counterparts. They are more flexible, faster, and allow execution of complex queries.
Brief history of Graph Databases
This technology has been around for a long time, but they've only recently started to gain in popularity.
The first graph database was conceptualized in the 1960s by Dr. E.F. Codd, who was also the founder of relational database management systems (RDBMS).
His concept, "A Relational Model of Data for Large Shared Data Banks", first appeared in a journal of the Association for Computing Machinery in June 1970. However, graph databases didn't really take off until the early 2000s, when social media started to become popular.
Since then, they've continued to grow in popularity as more industries embrace them.
The main components of a Graph Database
There are about 4 main components that define a graph database:
- Node: A node is a representation of an entity, such as a person, place, or a product. They are the equivalent of a row in a traditional database. For example, in a social network, each person would be represented by a node.
- Edge: An edge is a connection between two nodes and represents the relationship between them. Still using the example above, an edge could represent friendship, family, schoolmate, or co-worker as the relationship that connects two persons on a social media platform.
- Property: This is the attribute or simply the details associated with a node. In the case of persons, it could be gender, age, date of birth etc. For products, it could be type, size, color or weight.
- Label: A label defines the category of a node.
The following image helps to describe these components:
Advantages of Graph Databases
Here are the top five advantages of using graph databases:
Graphs are not just so good in organizing data. Their performance is super good and here are some of the ways these databases deliver on performance.
- Simple to query: In a traditional database, data is typically stored in rows and columns. This can make it difficult to find specific information, as you have to scan through all of the data in order to find what you're looking for. But in a graph database, data is stored in a network format. This makes it easy to find specific information, as you can simply follow the path from the node you're interested in to the nodes that it's connected to.
- Excellent speeds: Graph databases are extremely fast. This is because of the structure which is such that data is stored in an interconnected web of nodes and edges. This translates to retrieval times that are often much faster than with traditional databases.
- Easy configuration: Graphs can be easily configured to store and query data in any way you need, making them perfect for handling complex data sets. This makes them a great choice for applications that require real-time analysis of data, such as recommendation systems and fraud detection.
Graph Databases are scalable
Graphs can be easily scaled up or down depending on the needs of the moment. In a traditional database, if you want to add a new field to your data you have to go through a process of changing the schema, loading the data into the new schema, and retesting all the applications that use that data.
With a graph database, you can simply add the new field to the graph and it will be automatically available to all applications.
Graph Databases provide rich insights
The graph format makes it easy to explore complex relationships between data points, which can help uncover hidden patterns and trends. This can be extremely valuable for making data-driven decisions about a business.
Additionally, graph databases can be used to generate reports and visualizations that make it possible to understand data in a more intuitive way. This can save companies a lot of time and money that would have been used to purchase expensive data analysis systems.
Graph databases are great for agile projects
Agile software development is all about speed and flexibility. Requirements can change quickly, and teams need to be able to respond quickly to those changes. That's where graph databases come in because teams need to be able to understand complex data relationships on the go.
And because data on a graph can be accessed and queried in any order, it's easy to make changes and modifications as a project progresses. This makes it a great choice for teams that work in a fast-paced, iterative environment.
Difference between a Graph Database and Relational Database
The key difference between graph databases and relational databases lies in the structure used to store data.
Relational databases store data in tables. The tables are made up of rows and columns. A row stores singular data points and the collection of multiple rows constitutes a table. So, for example, you might have a table with information about customers, and each row would contain details about a single customer such as their name, address, and phone number. This structure is good for storing data that can be broken down into distinct parts, such as customer information or product orders. However, it can be difficult to query these databases when the data is complex or interconnected. No relationship can be formed within the rows like a graph database. Examples of relational databases include MySQL and Microsoft SQL Server.
Graph databases on the other hand don't store data in tables. Instead, they store data in graphs. This structure makes it easier to query data that is connected or interrelated. Examples of graph databases include Neo4j, Amazon Neptune, Oracle NoSQL DB, and Microsoft Azure Cosmos DB.
Types of Graph Databases
Broadly, there are two types of GDB namely Resource Descriptive Framework (RDF) and Property GDB.
RDF graphs emphasize data integration and are the simplest type of graph databases. The Resource Description Framework (RDF) graph model structures data as a series of statements about resources.
Property graphs are designed mostly for analysis and querying. They are particularly well-suited for representing complex relationships between data points, making them perfect for data-rich applications like social media networks, recommendation engines, and fraud detection.
Property graphs are more complex than RDF graphs, as they allow any number of properties to be associated with each node. This makes them ideal for real-world data sets, as they allow the modeling of complex relationships between data items.
What is the difference between RDF graphs and property graphs?
RDF graphs are semantic, while property graphs are not. This means that RDF graphs can be processed by machines to extract meaning, while property graphs cannot. This is why RDF graphs are typically used for representing data in the semantic web where the structure of data is important, while property graphs are more commonly used in business intelligence and data mining applications where the relationships between data items are important.
So, RDF graphs are good at representing information in a way that is easy for computers to understand, while property graphs are good at representing information in a way that is easy for humans to understand.
How Graph Databases work
When data is added to a graph database, it is stored in nodes and edges. As we have seen, nodes are the basic unit of data in a graph database, and represent entities such as people, businesses or products. Edges are the connections between nodes, and represent the relationships between entities.
This arrangement allows easy querying and analysis of data by following the relationships between nodes. For example, a graph could be used to find out how many friends a person has on Facebook, or what products are being sold on Amazon by businesses that are also selling products on eBay.
Here are steps to demonstrate how a graph database works:
- A graph database is a collection of nodes and edges.
- Nodes are the graphical representations of data, while edges are the lines that connect them.
- Data is stored in the nodes, and each node has a unique ID.
- Edges also have IDs, which identify the relationship between two nodes.
- When a database is queried, the start node and the end node must be specified.
- The database then returns all the nodes and edges that are connected to those nodes.
There are key features or parts that together make a graph database work. These are the graph processing engine, query language, graph algorithm library, and administrative interface;
- Graph processing engine: The graph processing engine is the heart of a graph database. It is responsible for querying, traversing, analyzing and processing the links and relationships in the data. The engine needs to be fast and efficient so that it can handle large amounts of data. It also needs to be able to scale up and down as needed, so that it can cope with fluctuations in real-time.
- Query language: This is the language that is used to query the database and find the data that is needed. There are many different types of query languages, but some of the most popular ones include Cypher and Gremlin.
- Graph algorithm library: This library is responsible for all the complex mathematical computations that are needed to analyze and traverse graphs. These libraries typically provide a variety of algorithms, including those for graph traversal, search, network flow, and more. Some graph algorithm libraries also include support for parallel processing, which can be essential for large-scale graph analytics.
- Administration interface: This is where the database is managed, including adding and removing nodes and edges, and changing properties. The interface may also provide some level of data security, like encryption and access control. In addition, the administration interface may be used to tune graph database performance. For example, the administrator can set the storage size, page cache size, and other parameters that affect how the database performs.
Why does your business need a Graph Database?
If your business relies on data with lots of relationships and the need to query these relationships on the go, then you certainly need graph databases.
Why are relationships important in business? The ability to quickly see the relationships between different pieces of data means you are able to make timely and better decisions because you'll have a better understanding of how everything is connected.
For example, let's say you're a retailer and you want to know which products are most popular with your customers. A graph database can help you find that information quickly. It can also help you identify deeper relationships between products and customers, so you can create targeted marketing campaigns.
Graph databases offer a quick way to identify patterns, anticipate changes and respond fast enough. This is important when you are trying to understand the kind of experience your customers are receiving from your business. It calls for personalizing the interactions as much as possible. Graph databases make it easier to do just that, as they allow you to track customer interactions and preferences in real-time. This means that you can quickly identify any patterns or trends and tweak your marketing strategies accordingly.
Popular use cases for Graph Databases
Virtually every industry can make use of the graph technology to great effect though some sectors like social media and finance are already far ahead. Here are the most popular uses:
Social media is by all means the pioneer sector that actually popularized the use of graph databases. These are some of the prominent ways social media companies are using graph technology:
- Building user profiles: Almost every social media platform has some form of user profile. This is where users store their personal information, such as name, email address, and contact information. Most social media platforms use graph databases to build these user profiles. They are perfect for this task because they can store and quickly access information about relationships between data points. This makes it easy to connect different pieces of data about a user.
- Making suggestions: Facebook, LinkedIn, and other social media platforms use graph databases to make personalized recommendations. For example, if you've liked pages for different types of restaurants, Facebook will start recommending restaurants to you. LinkedIn will show you people you may know based on your professional contacts.
- Ad targeting: By mapping out the relationships between people, brands, and other entities, social media companies are able to create a detailed picture of who the user is and what they like. This information is then used to serve targeted ads that are more likely to be of interest to the user. It's a win-win for both the advertiser and the user, as the user gets ads that are relevant and the advertiser gets more clicks and conversions.
- Fighting fake news: Social media companies are using graph databases to detect patterns in user behavior, identify fake accounts and fraudulent activity. This is achieved by analyzing relationships between users, posts, and comments to identify suspicious activity. For example, by analyzing the relationships between users who share fake news articles, social media companies can quickly identify and remove them from the platform.
Many people benefit from recommendation engines without knowing it. These engines are used to predict things like what one might want to buy or watch based on their past behavior.
There are many different types of recommendation engines, but one of the most popular is the collaborative filtering algorithm. This algorithm uses data from previous interactions between users and items to make recommendations.
Graph databases are perfect for collaborative filtering algorithms because they can easily identify relationships between users and items. This allows the engines to recommend items that a user is likely to be interested in, based on past behavior..
Money laundering and fraud detection
There are many different types of fraud, but they all have one thing in common: they involve some kind of illegal activity that is carried out in a pattern. And because graph databases are excellent in identifying patterns, they are being deployed in the hunt for frauds.
By mapping out the relationships between entities (e.g. customers, banks, products, etc.), graphs can quickly identify any unusual activity that may be indicative of fraud.
For example, if a particular customer has been making a lot of unusually large transactions, or if a bank starts processing more transactions than usual, a graph database can be used to track these activities and see if there is anything suspicious going on. A financial institution would use a graph database to track all of the transactions carried out by a customer, as well as the people and organizations involved in those transactions. By linking transactions together, patterns will emerge that may give crucial leads for money laundering culprits.
Another great use case for graph databases in fraud detection is social media analysis. By looking at the relationships between different accounts on social media, it’s possible to spot fraud rings that would be otherwise undetectable.
There are multiple relationships and dependencies in the manufacturing sector, which makes graph databases a valuable tool here.
The databases can identify bottlenecks and optimize the production process by mapping the relationships between machines, workers, parts, and products. They can also help identify which products are most popular so that the production schedule can be adjusted accordingly.
Traceability is another key area where graph technology is finding use in manufacturing. Manufacturers can create a «digital thread» that allows them to follow every product from start to finish by linking data from RFID tags, barcodes, and sensors to graphs. This not only helps them identify and solve problems quickly, but also makes it easier to comply with government regulations for product traceability.
Transportation and logistics
Like manufacturing, transportation and logistics is also full of many moving parts which can be streamlined efficiently by deploying graph technology. Here are some examples:
- Package tracking: Graph databases can be deployed to track packages more accurately and efficiently than traditional databases, making it easier to find misplaced or lost packages.
- Optimizing routes: Graph databases can help create more efficient delivery routes by mapping out the relationships between different stops and pickups, identifying which routes are busiest and when demand is highest.
- Tracking inventory levels: Graph technology can be used in supply chain management to track inventory levels at each stage of the supply chain, allowing companies to stockpile goods when prices are low and release them when prices are high.
Graphs are a natural fit for healthcare industry applications as they are perfect for managing and querying relationships between data points. Here are the most popular uses;
- Finding new drugs and treatments: Scientists can use graph databases to study the relationships between proteins, genes, and diseases in order to find new drugs and treatments.
- Preventing disease outbreaks: Graph databases can help public health officials track the spread of disease and identify potential outbreaks before they happen.
- Personalized medicine: Doctors can rely on graph technology to create individualized treatment plans for their patients based on their unique genetic makeup.
- Precision medicine: Researchers can identify genetic variants that are associated with specific diseases or medical conditions.
Cybersecurity experts have been using graph databases for a while now to fight against cybercrime, and this is increasing as cyber crimes explode. Here are 5 ways graph technology is being used in cybersecurity:
- Mapping data relationships: Security analysts can easily identify network vulnerabilities and potential threats by understanding how data is interconnected. For example, by tracing the relationships between logins, devices, and data files, graph databases can quickly highlight any malicious activity. They can also bring out commonalities between attacks, which can help security teams develop better defenses.
- Predicting future attacks: Data from past cyber attacks can be used to create patterns that can then be used to predict the behavior of cybercriminals.
- Tracking changes over time: Say a company has a graph database of the devices on their network. It’s possible to track which devices have been added or removed from the network, as well as when they were added or removed. This can make it easy to quickly spot any unauthorized changes and investigate them before they cause any damage.
According to data science professor Dr. Alin Deutsch of the University of California San Diego, graph database algorithms will drive the next phase of AI.
Graph databases are particularly great for AI because they provide a fast, easy way to model complex relationships between data. This is perfect for AI projects, which often require processing huge amounts of data and mapping complex networks.
Adoption of graph technology by big tech
Big data is exploding, and organizations are hellbent on implementing database solutions that meet today’s humongous data requirements. Due to this, graph technology is now being adopted by many software development organizations and tech giants. One of them is the e-commerce giant Amazon.
The graph database-powered Amazon Neptune is a high-performance database engine that allows storing of billions of relationships within a graph, working at milliseconds latency. It supports both RDF and Property GDB along with all the popular query languages. With such flexibility, developers can build graph queries in Neptune that can maneuver through complex and highly connected datasets.
What does the future hold for graph databases?
While the usage of graph databases is relatively early compared to traditional databases, this technology is quickly evolving into a bigger force as the following trends indicate:
- The rise of artificial intelligence: Graph databases will be increasingly used in conjunction with artificial intelligence and machine learning algorithms to make sense of large amounts of data.
- The growth of Internet of Things: As the number of devices connected to the internet is growing by the day, IoT developers can use graph databases to create systems that manage and analyze the massive amounts of data generated by these devices.
- The rise of microservices: A graph database is an ideal solution for managing data relationships within a microservices architecture. Since each service is autonomous and self-contained, it's easy to create and navigate the data relationships between services. This makes it possible to easily assemble complex applications out of multiple microservices.
Graph databases are particularly well-suited for these applications, so it's no surprise that they're quickly becoming the go-to choice for businesses looking to harness more compute power from data.
A report by by market intelligence firm Report Linker indicates that the global graph database market size is projected to reach $8.1 billion by 2028. The key driving factors for this growth include increasing demand for solutions that are capable of processing low latency queries as well as the emergence of knowledge networks.
The graph technology is helping organizations create connections and visualize patterns that might not have been possible to visualize with other types of databases. And so for any business or project that involves making sense of complex relationships in data, a graph database is the ultimate solution.