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Graph databases, part of Oracle’s converged database offering, eliminate the need to set up a separate database and move data. Analysts and developers can perform fraud detection in banking, find connections and link to data, and improve traceability in smart manufacturing, all while gaining enterprise-grade security, ease of data ingestion, and strong support for data workloads.
Oracle Autonomous Database includes Graph Studio, with one-click provisioning, integrated tooling, and security. Graph Studio automates graph data management and simplifies modeling, analysis, and visualization across the graph analytics lifecycle.
Oracle is named a leader.
See how Oracle's graph database makes it easy to explore relationships and discover connections in data by providing support for different graph structures, powerful analytics, and intuitive visualization.
Discover graph use cases across industries and categories, including financial services, manufacturing, and machine learning research.
Oracle provides support for both property and RDF knowledge graphs, and simplifies the process of modeling relational data as graph structures. Interactive graph queries can run directly on graph data or in a high-performance in-memory graph server. Extensive integration with Oracle Database, Oracle Autonomous Database, and third-party and open-source features make it simpler to apply and use graph analytics.
Explore relationships with more than 60 prebuilt algorithms. Use SQL, native graph languages, Java and Python APIs, and Oracle Autonomous Database features to create, query, and analyze graphs. Then, display connections easily in data to discover insights like customer trends and fraud detection, and then use interactive tools to publish and share analysis results.
Gain fine-grained security, high availability, easy manageability, and integration with all other data in business applications. Oracle provides sophisticated, multilevel access control for property graphs vertices and edges, and RDF triples. Oracle also aligns with applicable ISO and Worldwide Web Consortium standards for representing and defining graphs and graph query languages.
With Graph Studio, almost anyone can get started with graphs to explore relationships in data. Graph Studio removes barriers to entry by automating complicated setup and management, making data integration seamless, and by providing step-by-step examples for getting started, all while offering powerful algorithms, a speedy in-memory analytics server, and advanced visualization.
Oracle Graph Server and Client enables developers, analysts, and data scientists to use graphs within Oracle Database. It may also be used as a user managed graph environment with Oracle.
It includes a high-speed, in-memory, parallel server for property graph query and analytics, an RDF graph server and query UI to run SPARQL queries, and client components such as command-line shells for working with the graph API, a plugin for SQLcl to run PGQL queries, a Python client for Jupyter notebooks, interpreters for Apache Zeppelin notebook, and a graph visualization tool.
Create a graph of all bank accounts, then run graph queries to find all customers who have information that reveals criminal activity.
Use an RDF graph to create a metadata layer that helps determine whether the manufacturing names for parts indicate the same item, the items are related, or the items can be used interchangeably because of their similarities. Use a graph database to connect all relationships among different manufacturing parts and highlight connections and relevant information with graph algorithms.
The various steps in the data lifecycle can be tracked and navigated, vertex by vertex, by following the edges in a graph. Follow the data’s path, see where the information originally resided, was copied, and utilized, so data professionals can fulfil GDPR requests.
Relationships between customers and the products they buy can be laid out in a graph database—so it becomes fast and easy to run algorithms through the data to discover recommendations.
Building machine learning models requires augmented data, which can be created by running graph algorithms on a dataset that has been loaded into a graph database, and creating enriched data which can then be used for machine learning.
The Property Graph Views (PG views) feature was introduced in 21.1. With this feature, users can create a property graph view on database tables, without moving the data anywhere. This article explains the new features and enhancements in Oracle Graph Server and Client 21.3.