The only constant is change. As a data scientist, this fact is ever so present in the world of artificial intelligence. New technologies are always emerging and can either be a catalyst to more efficient solution (if they are the right fit) or an unnecessary source of headache.
You might have heard of graph database technology and wonder what or why, or even concluded that it was just another trend. Well I’m going to tell all the basics you need to know and let you decide.
What is Graph Database (GDB)?
GDB is a method of storing , accessing and navigating related datasets using the relationship between data as essential as the data itself. Its design is similar to a human brain that connects a whole matrix of ideas into a coherent whole. The ‘Brain’ provides an avenue for the machine to understand concept relationships in a faster, more powerful and complete way than a relational database.
Let’s break this down a little. Each node represents an entity (a person, place, thing, category or other piece of data), and each relationship represents how two nodes are associated.
A practical example would be twitter connecting 330 million monthly active users. The digram below shows how twitter users would be represented in a graph data base
Each node (labeled
User) belongs to a single person and is connected with relationships describing how each user is connected. As we see above, Peter and Emil follow each other, as do Emil and Johan, but although Johan follows Peter, Peter hasn’t (yet) reciprocated.
Why should you consider learning about or even using graph database ? Graph databases are suited to handle volume, variety, and velocity. Managing data as graphs is a particularly good fit when the use case involves modifying schemas and accommodating new features, data points, or sources.
- Performance: Using GDB, performance stays constant as data volume grows
- Flexibility: You can add to existing structure without endangering current functionality
- Agility: It can keep up with your daily demands, evolving with changing business requirements
The flexibility and agility with which graph databases deal with complex informational structures allows content engineers to build a more powerful ecosystem of intelligent content.
There is a great potential for graph databases to make content smarter and more connected than ever. Content engineers can use graph databases as a tool to orchestrate content blocks, create consistency of stories and knowledge, and establish standardized meaning for interconnected structures. This creates an ecosystem that is ready for intelligent content outputs such as:
- Designing personalized experiences
- Providing ease of access to content resources
- Enabling seamless search and discovery interactions with the system
- Efficient access to internal and external resources
- Topic clustering
- Contextualized content serving
- Conversational interactions
- Storing and analyzing customer context data
- Working with content sets within a highly-interconnected system
How Does GDB work?
In the graph world, connected data is equally (or more) important than individual data points. unlike other database management systems, relationship take first priority in graph database.
As a result, the relationships and connections are retained through ever part of the data lifecycle, from the idea to design in a logical model, to implementation in a physical model to operation in a database system.
This approach means the your application doesn’t have to infer connections. This results in simpler data models, yet more expressive than ones from relational databases.
Graph databases and the technologies related to their development add another layer of richness to the way we are able to represent real-world objects on a conceptual level. This is largely possible thanks to the graph — a mathematical construct used for modeling and solving a large array of problems related to networks in the field of physics, biology, sociology, and information technology.
Computer science uses graphs and graph theory to model relationships between data objects and represent various networked structures. Graphs are used to map relationships between linked objects, related people, clusters of chemicals, value-chains, and much more.
The graph structure consists of nodes connected by edges.
The real world is richly interconnected, and graph databases aim to mimic those sometimes-consistent, sometimes-erratic relationships in an intuitive way. That’s what makes the graph paradigm different from other database models: It maps more realistically to how the human brain maps and processes the world around it.
Graph databases are hardly a “one size fits all” solution. The type of data, use cases, and available resources should all be considered when deciding to move forward with the graph model or a more traditional data management solution, like the relational database.
AI and machine learning applications tremendously increase in value when configured to work against graph databases, because they can now understand and analyze the edges, or relationships, between entities within the dataset or the content set in the graph form.
I hope this has been helpful in better understanding the ideas behind Graph Database technologies. Just like in GDB, deciding what new skills to add to your toolbox or which technology to complement your projects, it’s equally important to prioritize relationship to existing framework . The goal is coherent!