Graph databases are an 18th century concept with a host of modern applications.
Used for tasks as diverse as dating sites and fraud detection, graph technology works by looking at relationships, not just data. But the idea behind them – or, at least, their theoretical basis – is attributed to Swiss-born mathematician Leonhard Euler, in 1735.
For almost 300 years, graph theory remained a mostly academic pursuit. But graphs have turned out to be an ingenious way of dealing with large volumes of data, and especially complex relationships between data.
In recent years, technologists have taken graph theory and created the graph database, a type of database where connections, as well as data, are first class citizens.
Graph databases are designed to efficiently store and query connected data by using a node and relationship-based format, making them particularly equipped to solve problems when understanding those connections are critical.
One of the key advantages of graph databases is that they can mimic the way the human brain processes and understands associations. By representing data as nodes and relationships, graph databases provide a more intuitive and natural way of working with connected data.
By recording links between data, as well as data itself, graph-based systems can quickly mine information and identify trends, making them a powerful tool for real-time analytics, as well as for mapping social networks, supply chain patterns, or even crime waves.
As a graph database looks at connections and relationships – known as edges – it takes just minutes, or even seconds, to answer queries that might take days using a conventional database system.
Graph databases (and specialized versions called native RDF triplestores that embody reasoning power) show great promise in knowledge discovery, data management and analysis. They reveal simplicity within complexity. When combined with text mining, their value grows tremendously. As the database ecosystem continues to grow, as more and more connections are formed, as unstructured data multiplies with fury, the need to analyze text and structure results inside graph databases is becoming an essential part of the database ecosystem. Today, these combined technologies are available and not just reserved for the big search engines providers. It may be time for you to consider how to better store, manage, query and analyze your own data. Graph databases are the answer.
It seems that it is only a matter of time before other industries that deal with huge amounts of data such as banking and finance, pharmaceuticals, defense and intelligence will also be using graph databases. In fact, detecting crimes and identifying insurance fraud with the help of networks, relationships and entities with graph data is sure to be an interesting task.
Apache AGE has taken a new type of approach to implement Graph database with PostgreSQL underlying.
Apache AGE
Apache AGE Github
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