Introduction to Neo4j
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1. The document discusses Neo4j, the world's most popular graph database. It highlights Neo4j's customers in top retail, financial, and software firms and its presence in Silicon Valley and global offices. 2. Neo4j is used both on-premises and in the cloud as a database-as-a-service. The document also discusses Neo4j's graph data science capabilities and its rise in popularity from 2010 to 2020. 3. Going forward, Neo4j is focusing on cloud services and positioning developers at the center of its strategy and products like Neo4j Aura and the Graph Data Science Library.
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Introduction to Neo4j
- 1. 01/12/2020
- 3. ML/AI & Graphs Synergy #1 Graph Platform Neo4j Customer Use Cases Unite Neo4j Neo4j SuperiorityGraph DB Popularity
- 5. 7/10 20/25 7/10 Top Retail Firms Top Financial Firms Top Software Vendors Anyway You Like It Creator of the Property Graph and Cypher language at the core of the GQL ISO project Thousands of Customers World-Wide HQ in Silicon Valley, offices include London, Munich, Paris & Malmo Industry Leaders use Neo4j On-Prem DB-as-a-Service In the Cloud
- 8. + + + The world’s most popular graph database, as a cloud service
- 9. Neo4j Graph Data Science Library Neo4j Database Neo4j Bloom ™
- 12. 2020-2030
- 13. Neo4j Aura Professional + + + + + The Shift To The Cloud
- 14. + + + + + The Shift To The Cloud Neo4j Aura Enterprise (Coming Soon)
- 15. Developers at the Center of all we do Developers at the center
- 16. Rise of(Graph) Data Science - Get more insight out of the data you already have. - Add relationships into your existing workflows. - Make better, more accurate predictions. Why Do Graph Data Science?
- 17. Neo4j Graph Data Science Library Scalable Graph Algorithms & Analytics Workspace Native Graph Creation & Persistence Neo4j Database Visual Graph Exploration & Prototyping Neo4j Bloom Practical Integrated Intuitive Rise of(Graph) Data Science Neo4j for Graph Data Science
- 18. 2014 2015 2016 2017 2018 2019 20202013 The Rise of the Graph Database 2010 - 2020: From Model to Category Full Potential of the Property Graph Model
- 19. Oct 2020 1. 2. 3. Rank 4. 5. 6. 7. 8. 9. 10. Neo4j Microsoft Azure Cosmos DB ArangoDB OrientDB Virtuoso Amazon Neptune JanusGraph GraphDB FaunaDB Dgraph DBMS Database Model Graph Multi-model Multi-model Multi-model Multi-model Multi-model Graph Multi-model Multi-model Graph Score Oct 2020 Sep 2019 51.33 32.01 5.55 5.47 2.57 2.48 2.40 2.09 1.79 1.68 +0.71 +0.34 -0.25 -0.01 +0.01 +0.13 +0.06 +0.67 +0.07 +0.06 Full Potential of the Property Graph Model The Rise of the Graph Database 2010 - 2020: From Model to Category
- 20. Highest possible scores in: ● Performance ● Scalability ● Workloads ● Data management ● Data loading/ingestion ● Queries/search ● Use cases ● API/extensibility ● Transactions ● High availability and disaster recovery ● Deployment options Full Potential of the Property Graph Model The Rise of the Graph Database 2010 - 2020: From Model to Category
- 21. Full Potential of the Property Graph Model The Rise of the Graph Database 2010 - 2020: Sweet Spot Use Cases
- 23. • • • • • • •
- 29. Graph Data Science is a science-driven approach to gain knowledge from the relationships and structures in data, typically to power predictions. Rise of(Graph) Data Science What is Graph Data Science?
- 30. Rise of(Graph) Data Science o Get more insight out of the data you already have. o Add relationships into your existing workflows. o Make better, more accurate predictions. Why Do Graph Data Science?
- 31. Neo4j Graph Data Science Library Scalable Graph Algorithms & Analytics Workspace Native Graph Creation & Persistence Neo4j Database Visual Graph Exploration & Prototyping Neo4j Bloom Practical Integrated Intuitive Rise of(Graph) Data Science Neo4j for Graph Data Science
- 32. Pathfinding & Search Similarity Community Detection Centrality & Importance Graph Embeddings Link Prediction 50+ Graph Algorithms & ML techniques Flexible Analytics Workspace Rise of(Graph) Data Science The Graph Data Science Library
- 33. • Degree Centrality • Closeness Centrality • Harmonic Centrality • Betweenness Centrality & Approx. • PageRank • Personalized PageRank • ArticleRank • Eigenvector Centrality • Shortest Path • Single-Source Shortest Path • All Pairs Shortest Path • A* Shortest Path • Yen’s K Shortest Path • Minimum Weight Spanning Tree • K-Spanning Tree (MST) • Random Walk • Breadth & Depth First Search • Triangle Count • Local Clustering Coefficient • Connected Components (Union Find) • Strongly Connected Components • Label Propagation • Louvain Modularity • K-1 Coloring • Modularity Optimization • Euclidean Distance • Cosine Similarity • Node Similarity (Jaccard) • Overlap Similarity • Pearson Similarity • Approximate KNN • Adamic Adar • Common Neighbors • Preferential Attachment • Resource Allocations • Same Community • Total Neighbors ... Auxiliary Functions: • Random graph generation • Graph export • One hot encoding • Distributions & metrics • Node2Vec • Random Projections • GraphSAGE Rise of(Graph) Data Science 50+ Graph Algorithms in Neo4j
- 34. Write Your Own Algorithms Machine Learning Algorithms Use matrix math and neural networks to learn the structure of your graph Train, store, and fit predictive models within Neo4j Leverage your embeddings to build nearest neighbors graphs Use our Pregel API to build your own algorithms using our infrastructure Graph Embeddings & Complex Data Types Model Catalog GDS 1.0 First enterprise library for graph data science. Neo4j 4.0 Compatibility GDS 1.2 GDS 1.1 Graph Mutability & Expressivity GDS 1.3 New Algorithms & RBAC Graph Native Learning GDS 1.4 Rise of(Graph) Data Science Introducing GDS 1.4
- 35. Graph algorithms to uncover trends and patterns Patterns Pointers Queries to answer questions with connected data Predictions Graph-native ML learns the topology of your graph to uncover new facts Rise of(Graph) Data Science Putting it all Together
- 36. NODES talks: - Graph native learning: introducing GraphSAGE & the model catalog - Write your own algorithms with the Pregel API - Getting graph questions answered through Neo4j Bloom Developers: - Developer Guides: neo4j.com/developer/graph-data-science/ - GDS Sandbox: sandbox.neo4j.com/?usecase=graph-data-science - GitHub: github.com/neo4j/graph-data-science Books: - Graph Algorithms: Practical Examples in Apache Spark & Neo4j: neo4j.com/graph-algorithms-book/ - GDS For Dummies: neo4j.com/graph-data-science-for-dummies Rise of(Graph) Data Science Neo4j Graph Data Science - Resources
- 38. 38
- 39. Community Vs Enterprise What is the difference Between Community vs Enterprise? Neo4j Enterprise Edition Neo4j Community Edition
- 40. Neo4j Enterprise Architecture What is the Neo4j Enterprise Architecture Neo4j Graph Data Science Neo4j Databas e Neo4j Bloom
- 41. Neo4j Runes Everywhere Where can I deploy Neo4j? On-Prem DaaS – Fully ManagedIn the Cloud (BYOL)
- 42. 42