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Saturday, April 24, 2021

Overview of Graph Databases

Overview of Graph DBs and their uses.  From the CACM.  Below the intro, more at the link. 

Understanding NoSQL Database Types: Graph Databases     By Alex Williams  in CACM

While originating as a subset of NoSQL or "Not Only SQL," graph databases represent a sharp closing of SQL and NoSQL demarcations. Graph technologies are exploding in its market size as more companies and developers take up their hybrid flexibility offerings. Those offerings: Intuitivity plus scalability with a high connection and robust data pattern.

While I won't go into depth on the formation of the 'SQL vs NoSQL' debate, you could quite accurately say that SQL represents data stored in rows and tables, while high-growth NoSQL is data stores arranged via nested documents as columnar schemas or key-value pairs. One is relational, the other not so much.

Graph databases are formed from nodes, properties, and relationships—all in a very interlinked data structure. And yet it supports advanced, rich querying with scalability. In this model, relationships matter just as much as the data itself. In a sense, it combines the querying power of relational databases with the intuitive flexibility of columnar non-relational databases—supporting agile development while also letting you gain deep insights.

Why use graph databases: The benefits

The graph model is a general-purpose data technology. While many know it for its social media implementations—this 'emerging shape', as it's known amongst data scientists due to being a non-typical dataset, has become most popular with social media companies for performing social network analyses, and for creating social graphs via companies like Facebook and Twitter who are particularly focused on the Six Degrees of Separation concept—graph databases are actually found in a large variety of industries, ranging from finance to healthcare, to emergency-response networks.

The principal benefit of graph databases is using its ability to assign values to links or connections. If your data has connections, whether for offline machine learning systems or online mobile applications, implementing this emerging shape will likely be beneficial.

In short: Build high-fidelity, highly interconnected networks made of bite-sized, scalable patterns (ie. great for CI/CD dev) that can together service, query, and manage sophisticated problem domains.  ... ' 

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