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Neo4j 4 Overview

Ivan Zoratti, Neo4j
Riccardo Ciarlo, Neo4j

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Neo4j 4 Overview

  1. 1. Neo4j 4 Overview Webinar Riccardo Ciarlo & Ivan Zoratti October 29th, 2020
  2. 2. Introductions Ivan Zoratti Director of Product Management Riccardo Ciarlo Territory Manager Italy
  3. 3. • Introduzione di Neo4j • Cos'è un Database a Grafo • Quali sono i principali casi d'uso e come Neo4j li rende possibili, efficaci e veloci • Come si esplorano e visualizzano i Grafi • Come risulta semplice creare le query e sottoporle al Database Neo4j • Domande e discussione Agenda
  4. 4. Neo4j, the graph company
  5. 5. Neo4j - The Graph Company The Industry’s Largest Dedicated Investment in Graphs Creator of the Market Leading Neo4j Graph Database Platform ~ 380 employees HQ in Silicon Valley, and offices in London, Munich, & Malmo + 400 Global Enterprise Customers
  6. 6. Connections in Data are as valuable as the Data itself Networks of People Transaction Networks Bought Bought Viewed Returned Bought Knowledge Networks Plays Lives_in In_sport Likes Fan_of Plays_for Know s Knows Knows Knows
  7. 7. Harnessing Connections Drives Business Value Enhanced Decision Making Hyper Personalization Massive Data Integration Data Driven Discovery & Innovation Product Recommendations Personalized Health Care Media and Advertising Fraud Prevention Network Analysis Law Enforcement Drug Discovery Intelligence and Crime Detection Product & Process Innovation 360º view of customer Compliance Optimize Operations Data Science AI & ML Fraud Prediction Patient Journey Customer Disambiguation Transforming Industries
  8. 8. Neo4j is an enterprise-grade native graph database and associated tools: • Store, reveal and query data and data relationships • Traverse and analyze data to many levels of depth in real-time • Add context to AI systems and network structures to data science Native Graph Technology • • • • • • • •
  9. 9. The Whiteboard Model Is the Physical Model
  10. 10. Nodes • Can have Labels to classify nodes • Labels have native indexes Relationships • Relate nodes by type and direction Properties • Attributes of Nodes & Relationships • Stored as Name/Value pairs • Can have indexes and composite indexes • Visibility security by user/role Neo4j Invented the Labeled Property Graph Model
  11. 11. Relational Versus Graph Models Relational Model Graph Model KNOWS KNOWS KNOWS ANDREAS TOBIAS MICA DELIA ANDREAS DELIA TOBIAS MICA
  12. 12. What Is Different in Neo4j? Index-Free Adjacency
  13. 13. TRAVERSE READ WRITE Security and Data Privacy Baseline_Personnel _Security_Standard Security_Check Counter_Terrorism _Check Developed_Vetting
  14. 14. Security and Data Privacy in Practice
  15. 15. High Availability and Unbounded Scalability
  16. 16. Causal Clustering with Neo4j
  17. 17. Introducing Sharding and Federated Graphs
  18. 18. Robust Graph Algorithms • Run on the loaded graph to compute metrics about the topology and connectivity • Highly parallelized and scale to 10’s of billions of nodes The Neo4j GDS Library Mutable In-Memory Workspace Computational Graph Native Graph Store Efficient & Flexible Analytics Workspace • Automatically reshapes transactional graphs into an in-memory analytics graph • Optimized for analytics with global traversals and aggregation • Create workflows and layer algorithms
  19. 19. +50 Algorithms in the Neo4j GDS Library • 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 • Degree Centrality • Closeness Centrality • CC Variations: Harmonic, Dangalchev, Wasserman & Faust • Betweenness Centrality & Approximate • PageRank • Personalized PageRank • ArticleRank • Eigenvector Centrality • Triangle Count • Clustering Coefficients • Connected Components (Union Find) • Strongly Connected Components • Label Propagation • Louvain Modularity • K-1 Coloring • Euclidean Distance • Cosine Similarity • Node Similarity (Jaccard) • Overlap Similarity • Pearson Similarity • Approximate KNN Pathfinding & Search Centrality / Importance Community Detection Similarity Link Prediction • Adamic Adar • Common Neighbors • Preferential Attachment • Resource Allocations • Same Community • Total Neighbors ...and also Auxiliary Functions: • Random graph generation • Encoding • Distributions & metrics
  20. 20. Neo4j Cloud offerings to suit every need Database-as-a-service Self-hosted Cloud Managed Services (CMS) Cloud-native service Zero administration Pay-as-you-go Self-service deployment Cloud-native stack No access to underlying infra and systems. Self hosted and managed Any cloud (AWS, GCP, Azure) Bring-your-own-license Self-manage software, infra in own private cloud Own data, tenant, security >50% deploy this way White-glove fully managed service by Neo4j experts Fully customizable deployment model and service levels Operate In own data centers or Virtual Private Cloud
  21. 21. Neo4j Aura: Built for the best developer experience Neo4j’s open source roots backed by the strongest graph community helps deliver the best developer experience to rapidly build rich graph-powered applications Easy Start in minutes Automatic upgrades, patches Scale on-demand instantly Zero downtime Powerful Lightning-fast queries with Native graph engine Flexible “whiteboard” data model Cypher - expressive, efficient and easy! Broad language driver support Reliable End-to-end encrypted Always ON Globally available on world-class infrastructure Self-healing, durable ACID compliant Affordable Pay-as-you-go Capacity based pricing Billing by the hour, starting as low as 9¢/hr Simple and predictable bills
  22. 22. Querying and Integrating Plugins and Extensions Scalable Graph Algorithms & Analytics Workspace Native Graph Creation & Persistence Visual Graph Exploration & Prototyping Neo4j Bloom Performance and flexibility Simplicity and integration Intuitive Drivers and Connectors
  23. 23. Cypher: Powerful & Expressive Query Language
  24. 24. MATCH (boss)-[:MANAGES*0..3]->(sub), (sub)-[:MANAGES*1..3]->(report) WHERE = “Jane Doe” RETURN AS Subordinate, count(report) AS Total Express Complex Queries Easily with Cypher
  25. 25. Explore & Collaborate with Neo4j Bloom Explore Graphs Visually Prototype Concepts Faster Collaborate Across Teams
  26. 26. Neo4j Bloom’s Intuitive User Interface Search with type-ahead suggestions Flexible Color, Size and Icon schemes Visualize, Explore and Discover Pan, Zoom and Select Property Browser and editor
  27. 27. Native Graph Technology for Applications & Analytics
  28. 28. The New Journey: Neo4j Version 4 ALIGNED
  29. 29. Recommendations Dynamic Pricing IoT-applicationsFraud Detection Real-Time Transaction Applications Generate and Protect Revenue Customer Engagement Metadata and Advanced Analytics Data Lake Integration Knowledge Graphs for AI Risk Mitigation Generate Actionable Insights Network Management Supply Chain Efficiency Identity and Access Management Internal Business Processes Improve Efficiency and Cut Costs Graph Use Cases by Value Proposition
  30. 30. Handling Large Graph Work Loads for Enterprises Real-time promotion recommendations Marriott’s Real-time Pricing Engine Handling Package Routing in Real-Time
  31. 31. Improving Analytics, ML & AI Across Industries Meredith Marketing to the Anonymous Financial Fraud Detection & Recovery Top 10 Bank AstraZeneca Patient Journeys
  32. 32. Let’s Do Something Amazing Together… Try Neo4j today: Free training and education: Contact us: