Graph Database and Graph Analytics

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.

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Introducing Oracle's graph database

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.

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Analyze connections and find patterns in data with graphs

Learn how Oracle Graph gives you insights into data when modeling it as a graph and analyzing the connections in the data.

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Try property graphs with Oracle Database 23c Free—Developer Release

Explore connections in data using simple SQL queries. Try the new property graph feature in Oracle Database 23c for free.

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Get started with graph analytics in minutes

Get code examples to easily create graphs from relational tables, query, analyze, and visualize graphs—all using Graph Studio in Autonomous Database.

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Try operational property graphs in Oracle Database 23c Free

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Why a graph database from Oracle?

Complete graph database

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.
Comprehensive 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.
Enterprise-level scalability and security

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.

Graph database and graph technologies

Graph Studio in Oracle Autonomous Database

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.

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Key features include:

Automated graph modeling
Automated install, upgrade, and provisioning
Autosave, backup, and checkpoint data restoration features
Advanced notebooks and visualization
Ability to schedule analysis
Sample notebooks and workflows for different use cases

Graph in Oracle Database

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.

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Oracle Graph Server and Client includes:

Graph Server
Graph Client
Graph Client for Apache Zeppelin
Graph HDFS Connector
PGQL Plugin for SQLcl
Graph visualization application
RDF Server and Query UI
Graph PL/SQL patch

Oracle named a Leader in the Forrester Wave™: Graph Data Platforms, Q4 2020

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Certegy fights payment fraud using Oracle Graph

Certegy uses Oracle Graph Studio to apply pattern recognition and statistical analysis of complex relationships to track and block accounts with fraudulent activity.

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Paysafe manages fraud and analyzes payment networks efficiently with Oracle Graph

"Queries that used to take minutes, hours, or even days now run in sub-seconds with Oracle’s graph features.”

Yavor Ivanov, Head of Database Administration, Paysafe Group

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"Oracle’s graph database easily achieved scalability while managing massive amounts of user entity information and their keys."

Toshihiro Yamashita, CEO, Amenidy Inc.

"Graph Studio’s point-and-click, low-code user interface enables us to cast traditional relational tables as a graph, allowing us to capitalize on Oracle's comprehensive set of graph algorithms."

Dan Vlamis, President, Vlamis Software Solutions

"With Graph Studio in Oracle Autonomous Database, you don't need to be a graph expert to explore the power of graphs."

Gianni Ceresa, Managing Director of DATAlysis and Oracle ACE Director

Graph database use cases

Money laundering detection for financial services

Create a graph of all bank accounts, then run graph queries to find all customers who have information that reveals criminal activity.

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Master data management in manufacturing

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.
Data lineage tracking for GDPR

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.
Product recommendations in marketing

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.
Feature engineering for machine learning

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.