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Introduction to Graph Databases

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DataStax
DataStax

This document discusses graph databases and introduces DataStax Enterprise Graph. It defines a graph database as one that prioritizes relationships between entities over the entities themselves. It provides examples of problems well-suited for graph databases, such as customer 360 views, recommendations, and fraud detection. The document contrasts graph and relational databases, noting graphs are better for highly connected data. It then introduces DataStax Enterprise Graph as a native graph implementation built on Apache TinkerPop and integrated with Cassandra for scale-out performance and DSE's enterprise features.

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Introduction to Graph Databases Robin Schumacher VP Products
©2016 DataStax Confidential. Do not distribute without consent. • A database for storing, managing and querying highly connected and complex data. • A graph database’s architecture makes it particularly well suited for exploring data to find commonalities and anomalies in large data volumes and unlocking the value in the data’s relationships. What is a Graph Database?
• How can I smartly understand and analyze my business data and their relationships that are consolidated across various business units and get a holistic view of my customer? • MDM usage for graph include product catalogs and customer 360 applications, which often have complex hierarchical structures or other relationships. ©2016 DataStax Confidential. Do not distribute without consent. Examples of Graph-Shaped Business Problems Master Data Management / Customer 360
• How can I quickly and most effectively influence customers to buy my products and recommend them to others? • A graph is best when recommending products, next actions, or advertising based on a user's data, relationships, past behavior, and interactions is important. ©2016 DataStax Confidential. Do not distribute without consent. Examples of Graph-Shaped Business Problems Recommendation/Personalization
• How can I quickly determine which entity, transaction or interaction is fraudulent, poses a security risk, or is a compliance concern? • In a complex and highly interrelated network of users, entities, transactions, events, and interactions, a graph database can quickly find the bad needle in a haystack of relationships and events that involve countless financial interactions. ©2016 DataStax Confidential. Do not distribute without consent. Examples of Graph-Shaped Business Problems Security and Fraud Detection
• How can I easily perform analysis on numerous relationships that form among data elements and tend to be of much greater interest when examined collectively than reviewed in isolation? • A graph is also a good model for managing network assets (with their properties or configurations) and how they relate to each other over time. ©2016 DataStax Confidential. Do not distribute without consent. Examples of Graph-Shaped Business Problems IoT, Asset Management and Networking
How Does Graph Compare to Other Databases?
©2016 DataStax • A key difference between a graph database and an RDBMS is how relationships between entities/vertexes are prioritized and managed. • While an RDBMS uses foreign keys to connect entities in a secondary fashion, edges (the relationships) in a graph database are of first order importance. • Relationships are explicitly embedded in a graph data model. • A graph-shaped business problem is one in which the concern is with the relationships (edges) among entities (vertexes) than with the entities in isolation. Graph vs. RDBMS RDBMS Graph An identifiable “something” or object to keep track of Entity Vertex A connection or reference between two objects Relationship Edge A characteristic of an object Attribute Property
©2016 DataStax RDBMS vs. Graph RDBMS Graph Simple to moderate data complexity Heavy data complexity Hundreds of potential relationships Hundreds of thousands to millions or billions of potential relationships Moderate JOIN operations with good performance Heavy to extreme JOIN operations required Infrequent to no data model changes Constantly changing and evolving data model Static to semi-static data changes Dynamic and constantly changing data Primarily structured data Structured and unstructured data Nested or complex transactions Simple transactions Always strongly consistent Tunable consistency (eventual to strong) High availability (handled with failover) Continuous availability (no downtime) Centralized application that is location dependent (e.g. single location), especially for write operations and not just read Distributed application that is location independent (multiple locations involving multiple data centers and/or clouds) for write and read operations Scale up for increased performance Scale out for increased performance (for some graph DB’s)
©2016 DataStax Graph vs. NoSQL Databases
©2016 DataStax • A scale-out graph database for cloud applications that need to manage complex and highly connected data. • Supports a property graph model native inside the DataStax product, engineered specifically for Cassandra. • Store & find relationships in data fast and easy in large graphs. • Built-in support for real-time search, and analytic graph queries via tight integration with DSE. • Part of DSE’s multi-model platform. What is DataStax Enterprise (DSE) Graph?
12 DataStax Enterprise © DataStax, All Rights Reserved.
• Built with Apache TinkerPop – the industry standard graph framework and language, contributed to Apache by DataStax. • Integrated deeply with Cassandra – inherits all of Cassandra’s capabilities. • Inspired by Titan – acquisition of Titan developers Aurelius has paid off with deep integration of their graph expertise with ours in Cassandra, search, and analytics. • Enterprise ready – integrated with all of DSE’s enterprise features including advanced security, built-in analytics, and search, automated workload management, visual management, visual development, integrated drivers, and expert support. • Multi-model – graph support is augmented by key-value, tabular, and JSON / Document model support in one platform. Benefits of DataStax Enterprise Graph
A Complete Integrated Solution for Graph © 2016 DataStax, All Rights Reserved. 14 Server Visual Management/Monitoring Visual Development Integrated Drivers (CQL, Gremlin, etc.) Java Python C++ More…
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More Related Content

Introduction to Graph Databases

  • 1. Introduction to Graph Databases Robin Schumacher VP Products
  • 2. ©2016 DataStax Confidential. Do not distribute without consent. • A database for storing, managing and querying highly connected and complex data. • A graph database’s architecture makes it particularly well suited for exploring data to find commonalities and anomalies in large data volumes and unlocking the value in the data’s relationships. What is a Graph Database?
  • 3. • How can I smartly understand and analyze my business data and their relationships that are consolidated across various business units and get a holistic view of my customer? • MDM usage for graph include product catalogs and customer 360 applications, which often have complex hierarchical structures or other relationships. ©2016 DataStax Confidential. Do not distribute without consent. Examples of Graph-Shaped Business Problems Master Data Management / Customer 360
  • 4. • How can I quickly and most effectively influence customers to buy my products and recommend them to others? • A graph is best when recommending products, next actions, or advertising based on a user's data, relationships, past behavior, and interactions is important. ©2016 DataStax Confidential. Do not distribute without consent. Examples of Graph-Shaped Business Problems Recommendation/Personalization
  • 5. • How can I quickly determine which entity, transaction or interaction is fraudulent, poses a security risk, or is a compliance concern? • In a complex and highly interrelated network of users, entities, transactions, events, and interactions, a graph database can quickly find the bad needle in a haystack of relationships and events that involve countless financial interactions. ©2016 DataStax Confidential. Do not distribute without consent. Examples of Graph-Shaped Business Problems Security and Fraud Detection
  • 6. • How can I easily perform analysis on numerous relationships that form among data elements and tend to be of much greater interest when examined collectively than reviewed in isolation? • A graph is also a good model for managing network assets (with their properties or configurations) and how they relate to each other over time. ©2016 DataStax Confidential. Do not distribute without consent. Examples of Graph-Shaped Business Problems IoT, Asset Management and Networking
  • 7. How Does Graph Compare to Other Databases?
  • 8. ©2016 DataStax • A key difference between a graph database and an RDBMS is how relationships between entities/vertexes are prioritized and managed. • While an RDBMS uses foreign keys to connect entities in a secondary fashion, edges (the relationships) in a graph database are of first order importance. • Relationships are explicitly embedded in a graph data model. • A graph-shaped business problem is one in which the concern is with the relationships (edges) among entities (vertexes) than with the entities in isolation. Graph vs. RDBMS RDBMS Graph An identifiable “something” or object to keep track of Entity Vertex A connection or reference between two objects Relationship Edge A characteristic of an object Attribute Property
  • 9. ©2016 DataStax RDBMS vs. Graph RDBMS Graph Simple to moderate data complexity Heavy data complexity Hundreds of potential relationships Hundreds of thousands to millions or billions of potential relationships Moderate JOIN operations with good performance Heavy to extreme JOIN operations required Infrequent to no data model changes Constantly changing and evolving data model Static to semi-static data changes Dynamic and constantly changing data Primarily structured data Structured and unstructured data Nested or complex transactions Simple transactions Always strongly consistent Tunable consistency (eventual to strong) High availability (handled with failover) Continuous availability (no downtime) Centralized application that is location dependent (e.g. single location), especially for write operations and not just read Distributed application that is location independent (multiple locations involving multiple data centers and/or clouds) for write and read operations Scale up for increased performance Scale out for increased performance (for some graph DB’s)
  • 10. ©2016 DataStax Graph vs. NoSQL Databases
  • 11. ©2016 DataStax • A scale-out graph database for cloud applications that need to manage complex and highly connected data. • Supports a property graph model native inside the DataStax product, engineered specifically for Cassandra. • Store & find relationships in data fast and easy in large graphs. • Built-in support for real-time search, and analytic graph queries via tight integration with DSE. • Part of DSE’s multi-model platform. What is DataStax Enterprise (DSE) Graph?
  • 12. 12 DataStax Enterprise © DataStax, All Rights Reserved.
  • 13. • Built with Apache TinkerPop – the industry standard graph framework and language, contributed to Apache by DataStax. • Integrated deeply with Cassandra – inherits all of Cassandra’s capabilities. • Inspired by Titan – acquisition of Titan developers Aurelius has paid off with deep integration of their graph expertise with ours in Cassandra, search, and analytics. • Enterprise ready – integrated with all of DSE’s enterprise features including advanced security, built-in analytics, and search, automated workload management, visual management, visual development, integrated drivers, and expert support. • Multi-model – graph support is augmented by key-value, tabular, and JSON / Document model support in one platform. Benefits of DataStax Enterprise Graph
  • 14. A Complete Integrated Solution for Graph © 2016 DataStax, All Rights Reserved. 14 Server Visual Management/Monitoring Visual Development Integrated Drivers (CQL, Gremlin, etc.) Java Python C++ More…