Introduction to Graph Database
A Graph Database is characterized as a specific, single-purpose platform for making and manipulating graphs purpose-built to treat the relationship between data. It holds the data without restricting it to a predefined model. It uses nodes to store data entities and edges to store relationships between the entities. An edge consists of the start node, end node, type, direction and describes relationships, actions, and ownerships.
A graph in a graph database may be traversed on specific edges or across the complete graph. Traversing the joins or relationships is incredibly quick as the relationships between nodes are not determined at query times however they are persisted within the database. Graph databases have benefits in cases like social networking, recommendation engines, and fraud detection, once you need to produce relationships between data and quickly query these relationships.
What are the types of Graph Databases?
Popular Graph databases are mentioned below:
- Neo4j
- Microsoft Azure CosmosDB
- OrientDB
- ArangoDB
- Virtuoso
- JanusGraph
- Amazon Neptune
- GraphDB
- Giraph
- AllegroGraph
There are two popular types of Graph databases, Property graphs, and RDF graphs. The property graph is used for querying and analytics, and the RDF graph emphasizes data integration.
Property Graphs
The vertices contain itemized information about a subject, and edges indicate the connection between the vertices. The vertices and edges have attributes, which are known as properties. Property graphs are essentially used to demonstrate relationships among data, and they enable query and data analytics based on these relationships. This is utilized in many businesses and areas, like finance, manufacturing, public safety, retail, and numerous others.
RDF Graphs
RDF stands for Resource Description Frameworkthat conforms to W3C (World Wide Web Consortium) standards designed to represent statements. They are best for representing complex metadata and master data. RDF has features like data merging, even if the underlying schemas differ. It explicitly upholds the evolution of schemas over the long run. RDF has a unique terminology for naming nodes and edges in a graph. An edge is known as a triple, the source node is known as a subject, the edge name is known as a predicate, and the target node is known as an object. The RDF model empowers an approach to distribute the data in a standard format with distinct semantics, which permits data exchange. RDF graphs are widely adopted and used in Government statistics agencies, pharmaceutical organizations, and medical care sectors.
Explore more about Composable Data Processing with a Case study
What is Graph Database?
To understand Graph Theory, one does not need to understand graph theory's whole complex mathematical wizardry. They are more intuitive to understand than RDBMS. A graph is composed of two elements, and they are Nodes and Relationships. Each node in the graph represents an entity, whereas the relationship represents how two nodes are associated. For example, two nodes `engines` and `cars would have the relationship `are pointing from `engines` to `cars.` Another real-life example is Twitter which has a graph database connecting 330 million active users monthly.
We can understand it by the illustration below, where we have a small slice of Twitter users represented in a graph database. Each node labeled as `User` belongs to a single person and is connected with relationships describing how each user is connected. As we can see below, P1 and P3 follow each other, and so do P2 and P3, but only P3 follows P1, P1 does not follow P2 back.
Graph Vs. Relational Database
| Type | Relational DB | Graph DB |
| Format |
Tables with Rows and Columns |
Nodes, Relationships, Labels, Properties |
| Relationships |
Related across tables, set up utilizing foreign keys between tables |
Represented by relationships between edges and nodes |
| Architecture |
Relational - Example of a demo Students and Department tables
|
Graph - example of a demo Person and 3 departments as nodes
|
| Pros & Cons |
|
1.Flexible Schema 2. High performance for complex transactions 3.High performance for deep analytics 4.Do not require joins |
| Query Pattern | SQL Statement: SELECT name FROM Person LEFT JOIN Person_Department ON Person.Id = Person_Department.PersonId LEFT JOIN Department ON Department.Id = Person_Department.DepartmentId WHERE Department.name = "IT Department" |
Cypher Statement: MATCH (p:Person)-[:WORKS_AT]->(d:Dept) WHERE d.name = "IT Department" RETURN p.name |
| Top Use Cases |
Transaction focused use cases, including online transactions and accounting |
Relationship-heavy use cases, including fraud detection and recommendation engines |
What are the Advantages of Graph Database technology?
The graph format provides a flexible platform for discovering distant connections and dissecting data based on the strength or quality of relationships. It allows you to explore patterns and connections of social networks, IoT, big data, and complex transaction data for various business use cases.
Graph databases store the relationships, queries, and algorithms, utilizing the connectivity between vertices, it tends to run in sub-seconds. In other cases, it might require hours or days. Forget about countless joins, and also, the data can be more easily used for analysis and machine learning. Complex relationships can be evaluated easily for deeper insights with the help of graph format. Graph databases run queries mostly in Property Graph Query Language (PGQL). Furthermore, a couple of more broadly acknowledged query languages are SPARQL, GraphQL, Gremlin, and Cypher.
In precise Graph databases:
- Finds the shortest path between two nodes.
- Determines the nodes that create the most activity.
- Analyze connectivity to identify the weakest points of a network.
- Analyze the state of the network or community based on connection density in a group
Read More about Data Catalog with Data Discovery
What are the properties of Graph Databases?
The two most essential properties of Graph databases that make them unique are:
- Graph Storage: Some graph databases use storage that is native and specifically designed for storing and managing graphs. Other graph technologies use relational or columnar storage layers. But native storage is way faster than non-native storage layer in terms of graph connections as it needs to be translated into a different data model.
- Graph Processing: The most efficient means of processing data in a graph is Native graph processing because, in the database, connected nodes physically point to each other. On the other hand, Non-Native graph processing engines utilize different means to perform CRUD tasks which are not efficient and optimized for dealing with connected data.
What are the limitations of Graph Databases?
Graph databases are not as useful for operational use cases, and there is room for improvement. They are not efficient at processing high volumes of transactions, and they are not good at handling queries that span the entire database. To overcome this issue of storing and retrieving business entities such as customers or suppliers in an optimized way, we would need to combine a graph database with a relational or NoSQL database.
Graph databases, when used alone, do not provide a Master Data Management or MDM solution. When that's the case, It just acts as a data store and doesn't empower a business-facing user interface to query or manage relationships. It will also not provide advanced match and survivorship functionality and data quality capabilities.
The facts confirm that graph databases permit you to look through data related to an individual record quickly. However, you won't perform mass analytics queries across every one of the relationships and records.
Click to explore Adopt or not to Adopt Data Mesh? - A Crucial Question
How do Graph Databases work and their Use-cases?
Relationships get priority in graph databases, unlike other database management systems. In graph databases, connected data is equally or maybe sometimes more critical than individual data points.
This connections-first approach to data means relationships and connections are persisted through every part of the data lifecycle, starting from the idea, logical model designing, actual model execution, query language operations, and persistence within a scalable, reliable database system.
This approach enables your application by not inferring data connections using foreign keys or out-of-band processing, like MapReduce. Hence by this, data models are simpler and more expressive than the ones you’d produce with relational databases or NoSql.
Fraud Detection
Discrete data points that include specific accounts, individuals, devices, or IP addresses mainly focus on traditional fraud prevention measures. But, the present modern fraudsters found ways to deal with and move away from detection by forming fraud rings that comprise stolen and synthetic identities. It is essential to look beyond individual data points to the connections that link them to detect and uncover such fraud rings.
There are no fraud prevention measures that are perfect, yet on the off chance that we look past individual data points to the connections that connect them, efforts will altogether improve. Graph Database reveals hard-to-distinguish patterns that overwhelm the power of a relational database by a huge margin.
Ventures and organizations use graph database sets to increase their existing fraud detection capabilities to battle an assortment of monetary wrongdoings, including bank fraud, credit card fraud, e-commerce fraud, insurance fraud, and money laundering in real-time.
Recommendation Engines
The key to the success of any online business is real-time recommendation engines. To make it possible in real-time requires the ability to correlate product, customer, inventory, supplier, logistics, and even social sentiment data. A real-time recommendation engine should have the ability to instantly capture any new interests shown in the customer’s current visit – something that batch processing can’t accomplish. It is trivial to match historical and session data in a graph database.
The key technology that enables real-time recommendations is the graph database, which leaves traditional relational databases behind at a quicker pace. Graph databases effectively outflank relational and other NoSQL data stores for connecting masses of the buyer and product data or connected data to be more exact to acquire insight into client needs and product trends.
Conclusion
The real world is so deeply interconnected, and graph databases target to copy those, occasionally steady or once in a while inconsistent connections naturally. This makes the graph paradigm not the same as other database models, and it maps more sensibly to how the human mind maps and processes its general surroundings.
While big data has increased over the past decade, Graph databases have also evolved as compute power. It’s becoming clearer day by day that graph databases will become the standard tool for analyzing complex data relationships. The ability to derive insights in increasingly complex ways makes graph databases a must-have for today’s needs and tomorrow’s successes as businesses and organizations continue pushing big data and analysis capabilities.
- Click to discover the Top 9 Challenges of Big Data Architecture
- Explore more about Composable Data Processing with a Case study
