Developers should consider using graph databases | InfoWorld
Graph databases are proven architectures for storing data with complex relationships. Why aren't more companies using them?
By Isaac Sacolick
Contributing Editor, InfoWorld
Twenty years ago, my development team built a natural language processing engine that scanned employment, auto, and real estate advertisements for searchable categories. I knew that we had a difficult data management challenge. The data in some ad types were relatively straightforward, like identifying car makes and models, but others required more inference, such as identifying a job category based on a list of skills.
We developed a metadata model that captured all the searchable terms, but the natural language processing engine required the model to expose significant metadata relationships. We knew designing a metadata model with arbitrary connections between data points in a relational database was complex, so we explored using object databases to manage the model.
What we were trying to accomplish back then with object databases can be done better today with graph databases. Graph databases store information as nodes and data specifying their relationships with other nodes. They are proven architectures for storing data with complex relationships.
Graph database usage has certainly grown during the past decade as companies considered other NoSQL and big data technologies. The global graph database market was estimated at $651 million in 2018 and forecasted to grow to $3.73 billion by 2026. But many other big data management technologies, including Hadoop, Spark, and others, have seen much more significant growth in popularity, skill adoption, and production use cases compared to graph databases. By comparison, the big data technology market size was estimated at $36.8 billion in 2018 and forecasted to grow to $104.3 billion by 2026. ... "
No comments:
Post a Comment