Oracle Autonomous Database and Oracle Database offer a graph database as part of the converged database offering, which eliminates 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 the enterprise-grade security, ease of data ingestion, and strong support for data workloads that Oracle provides.
Oracle's graph databases make it easier for analysts to explore relationships and discover connections in data. Oracle simplifies graph analytics by providing ways to support different graph structures, deploy powerful algorithms, and visualize results intuitively.
Perform analytics, represent complex metadata and linked data, and streamline the creation and exploration of property and RDF knowledge graphs with support for standards, close to 60 graph algorithms, and support for popular notebooks.
Model relational data as graph structures through data description or mapping languages. Use statements to map tables and views to nodes and edges with properties, use packages to map them to other graph structures, or directly load data from native graph file formats or comma-separated files in parallel.
Streamline graph management by using Oracle Database features to load, restore, and export graphs. Interactive graph tooling automatically saves work and enables analysts to schedule an analytics report or job.
Analyze and query graphs in a highly scalable, parallel, in-memory graph server for subsecond query response times. Interactive graph queries can run in the in-memory analytics server or directly on graph data in Oracle Database and Oracle Autonomous Database.
Extensive integration with Oracle Database, Oracle Autonomous Database, and third-party and open source features make it simpler to apply and utilize graph analytics. Use native graph languages, SQL, Java, and Python to integrate graphs into applications on enterprise-grade data management infrastructure.
Use SQL, native graph languages, Java and Python APIs, and Oracle Autonomous Database features to create, query, and analyze graphs.
Explore relationships with nearly 60 prebuilt algorithms. Algorithms include common graph analyses such as path-finding, ranking, and community detection. Use ontologies to represent facts and relationships in a domain.
Display connections easily in data to discover insights like customer trends, detect fraud, and more.
Use interactive tools to publish and share analysis results. Native graph interpreters in popular notebooks support a wide range of analytic workflows.
Attain fine-grained security, high availability, easy manageability, integration with all other data in business applications, and more.
Gain sophisticated, multilevel access control for fine-grained security of graphs, vertices, edges, and RDF triples.
Expect alignment with applicable ISO and Worldwide Web Consortium standards for representing and defining graphs and graph query languages. Oracle implements RDF, OWL, and SPARQL query language standards, and actively supports SQL/PGQ standardization.
Oracle Graph Server and Client enables users to use graphs within Oracle Database and Oracle Autonomous Database. 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, interpreters for Apache Zeppelin notebook, and a graph visualization tool.
"Oracle Database is an excellent fit since we can have one single source of truth for all our data—both graph and relational. Adding PGQL in top of it allows for us easy yet very powerful graph analytics. Identifying suspicious activity is mission critical for our business. Oracle Graph Database provides outstanding performance benefit to this. Queries that used to take minutes, hours, or even days now run in subseconds with Oracle’s graph features. The improvements in delivering anti-fraud alerts on time are orders of magnitude. This brings a lot of real business value."Yavor Ivanov, Head of Database Administration, Paysafe Group
Create a graph of all bank accounts, then run a simple graph query 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.
Oracle Database 21c makes it simpler to install, configure, and deploy graph features with the Graph Server and Client Kit. This simplified packaging makes it easier to deploy and develop Property Graph applications and includes a native SPARQL endpoint to query RDF graphs.