As the world becomes more connected, so does its data. The data-management tools and techniques of the past are not equipped to handle this non-uniform, semi-structured and highly interconnected data. Faced with this dilemma, software professionals have the option of struggling to fit everything into relational or key-value models, or instead to embrace the graph data model and steer their teams toward success.It’s not always an easy decision to throw away decades of experience and replace relational databases altogether, but it is straightforward to move the highly connected parts of the system to a graph database. The size and complexity of the data is inevitably fueling a movement toward polyglot persistence (data housed in the stores best equipped to handle it) instead of a single (relational) database. While polyglot requires a broader understanding of data issues, it yields tremendous benefits for data architecture and governance.
To put the drivers for graph database adoption into context, we see that valuable data is generated by the spread of social networks outside their walled garden and into the rest of the Web. The social graph, which entangles the intent, interest and consumption graphs, is driving online commerce and advertisement. Furthermore, the latest advancements in smartphones have rapidly grown the mobile graph tying the online world back to the real. The data generated is already in graph form as people friend each other, like, want, and buy things, and check in to places. In other words, they are creating relationships, the very thing graph databases excel at.
Graph databases have a simple structure, using just two types of objects: nodes and relationships. Users, things and places are modeled as nodes, and various kinds of labeled, directed relationships are created between them (for example, when friend requests are accepted, or things are bought). These simple structures form paths that can then be easily used to compute which of your friends should also be friends, or what new music your friends are buying or what books they’ve recently liked. The data is connected in simple ways, but traversals of these connections can answer very sophisticated questions.
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Keywords: SD Times