What is a Graph Database?
Graph databases allow data points to establish simultaneous connections with multiple other data points, unlike traditional relational databases that link to just one point at a time. This architecture enables users to discover relationships between data more readily—such as customer information from various sources or identifying family connections among multiple customers—resulting in speed and accuracy.
Why it matters
These databases are traditionally classified as NoSQL systems and are sometimes called triple stores because they use a special index that stores information about nodes, edges, and the relationships between them in groups of three. They are a natural fit for connected, context-rich workloads such as decision intelligence.
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Related concepts
A Business Context Graph (BCG) is an interconnected dataset that's been enriched with meaning. It enables Diwo's applications to apply reasoning against data sources, supporting complex decision-making processes. Traditional databases suffer from static, shallow context, which limits intelligence capabilities—BCG adds the semantic layer required for decision intelligence.
Big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software can't manage them. The concept is characterized by three key attributes: unusually high data volumes, high velocity (the rate at which data changes, is collected, and grows), and significant variety in dimensionality and format.
Data architecture comprises the components that collectively fulfill an organization's data needs, including acquisition, storage, preparation, and analysis. Modern data architecture has been substantially shaped by concurrent advancements in big data, machine learning/AI, and cloud computing. It is designed proactively with scalability and flexibility in mind, anticipating complex data needs.
Data fabric is an architectural approach addressing data silos through flexible, resilient integration of data sources across platforms and business users, ensuring data availability wherever needed. While not a single static technology, data fabric conceptually provides consistent visibility and unified controls for managing disparate data and diverse technologies deployed across multiple data centers and edge computing locations—both on-premises and cloud-based.