What do Amazon, Facebook, Google, IBM, Microsoft and Twitter have in common? They're all adopters of graph databases - a hot technology that continues to evolve.
Whether for Customer Analytics, Fraud Detection, Risk Assessment or another real-world challenge, the ability to quickly and efficiently explore, discover and predict complex relationships is a huge competitive differentiator for businesses today. Getting it done involves more than merely connected data – it’s about real-time and up-to-date correlation, detection and discovery. Organizations need to be able to transform structured, semi-structured and unstructured data and massive enterprise data silos into an intelligent, interconnected data network that can reveal critical patterns and insights to support business goals.
This elemental pain point – the need for real-time analytics for enterprises with enormous volumes of data – is fueling graph databases’ emergence as a mainstream technology being embraced by companies across a broad range of industries and sectors.
Since seeing early adoption by companies including Twitter, Facebook and Google, the graph database market has been heating up. Giant cloud service providers Amazon, IBM and Microsoft have added graph databases in the last two years, validating the industry’s growing interest in graph technology for easy and natural data modeling, easy-to-write queries to solve complex problems, and fast insights from interconnected data.
In fact, graph databases are the fastest growing category in all of data management, according to consultancy DB-Engines.com. A recent Forrestersurvey showed that 51 percent of global data and analytics technology decision-makers are employing graph databases.
All other database types (RDBMS, data warehousing, document DB, and key-value DB) started primarily on-premises and were welcomed before database-as-a-service was established. Now that the large cloud service providers are going all in on graph technology, graph database adoption is likely to keep accelerating.
Organizations are embracing the power of the graph for a simple reason: By storing data in a graph format -- including nodes, edges and properties -- graphs offer distinct advantages over other databases, including better and faster queries and analytics, simpler and more natural data modeling, simultaneous support for real-time updates and queries, and flexibility for evolving data structures.
Many different kinds of graph database offerings are available today and each offers unique features and capabilities, so it’s important to understand the differences.
Operational Graph Databases are products suitable for a broad range of enterprise-level transactional applications.
