This document discusses Neo4j, an enterprise-grade native graph database. It provides examples of how Neo4j is used by companies for knowledge graphs, master data management, content management, investigative journalism, and other use cases. It outlines key aspects of Neo4j including its native graph storage and processing, powerful and expressive Cypher query language, scalability features, and support for highly available clusters. Overall, the document promotes Neo4j as a graph database built for the enterprise.
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Enterprise Ready: A Look at Neo4j in Production at Neo4j GraphDay New York CityNeo4j
This document contains the agenda for an enterprise Neo4j training session in New York City on April 18, 2017. The agenda includes sessions on using graphs with Neo4j, working examples of transforming data, and a look at deploying Neo4j in production environments. Lunch is from 12:30-1:30 and a training session runs from 1:30-5:00pm.
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Data is both our most valuable asset and our biggest ongoing challenge. As data grows in volume, variety and complexity, across applications, clouds and siloed systems, traditional ways of working with data no longer work.
Unlike traditional databases, which arrange data in rows, columns and tables, Neo4j has a flexible structure defined by stored relationships between data records.
We'll discuss the primary use cases for graph databases
Explore the properties of Neo4j that make those use cases possible
Look into the visualisation of graphs
Introduce how to write queries.
Webinar, 23 July 2020
This document provides an overview of the Neo4j Graph Platform vision, including existing and upcoming products. It discusses Neo4j's long-term vision of being a graph platform beyond just a database, including tools for development and administration, analytics, and integrations. It also highlights some key existing products like the Neo4j browser and algorithms library, as well as upcoming capabilities like analytics integrations and better visibility of partner software.
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During this Big Data Warehousing Meetup, we discussed how graph databases work, shared some real world use cases, and showed a live demo of the world’s leading graph database, Neo4J. Pitney Bowes demonstrated their new MDM product developed on a graph database.
For more information, check out the other slides from this meetup or visit our website at www.casertaconcepts.com
Maximize the Value of Your Data: Neo4j Graph Data PlatformNeo4j
In this 60-minute conversation with IDC, we will highlight the momentum and reasons why a graph data platform is a breakthrough solution for businesses in need of a flexible data model.
Please join Mohit Sagar, Group Managing Director of CIO Network, as he hosts the conversation with Dr. Christopher Lee Marshall, Associate VP at IDC, and Nik Vora, Vice President of APAC at Neo4. During this very exciting discussion, you'll discover the insights and knowledge unlocked with the graph data platform.
5. Building the Cancer Research Data Commons with Neo4j: The Bento FrameworkNeo4j
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Data Mesh in Practice: How Europe’s Leading Online Platform for Fashion Goes ...Databricks
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A Connections-first Approach to Supply Chain OptimizationNeo4j
Neo4j is a graph database platform for connected data. The document introduces Neo4j and discusses how connected data and relationships between data are increasingly important for business value. It provides examples of how Neo4j is used by organizations for applications like fraud detection, personalization, and network analysis. The document also summarizes Neo4j's capabilities like real-time transaction processing, analytics, and visualization and highlights its native graph architecture and performance advantages over traditional databases. Finally, it briefly describes Neo4j's key architecture components and how it can be used for common data architecture patterns.
Watch Alberto's presentation from Fast Data Strategy on-demand here: https://goo.gl/CRjYuD
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Watch this session to learn more about:
• The vision behind the Denodo Platform
• The new data catalog and self-service features of Denodo Platform 7.0
• The new connectivity, data transformation, and enterprise-wide deployment features
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- Big data refers to large volumes of data from various sources that is analyzed to reveal patterns, trends, and associations.
- The evolution of big data has seen it grow from just volume, velocity, and variety to also include veracity, variability, visualization, and value.
- Analyzing big data can provide hidden insights and competitive advantages for businesses by finding trends and patterns in large amounts of structured and unstructured data from multiple sources.
This document describes a platform called Iyka dataSpryng that provides comprehensive analytics capabilities. It removes the need for complex and siloed analytic processes by allowing direct access and analysis of disparate data sources. Key features include a unified view of all data, knowledge portability to leverage ontologies and dictionaries, and self-service analytics. This empowers users and provides 2x more productivity and faster results compared to traditional analytic methods.
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This document summarizes a presentation about the graph database Neo4j. The presentation included an agenda that covered graphs and their power, how graphs change data views, and real-time recommendations with graphs. It introduced the presenters and discussed how data relationships unlock value. It described how Neo4j allows modeling data as a graph to unlock this value through relationship-based queries, evolution of applications, and high performance at scale. Examples showed how Neo4j outperforms relational and NoSQL databases when relationships are important. The presentation concluded with examples of how Neo4j customers have benefited.
How a Logical Data Fabric Enhances the Customer 360 ViewDenodo
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- Big data can be used to analyze line-of-business content from various sources like documents, emails, databases to gain insights.
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DATA SCIENCE AND BIG DATA ANALYTICSCHAPTER 2 DATA ANA.docxrandyburney60861
DATA SCIENCE AND BIG DATA
ANALYTICS
CHAPTER 2:
DATA ANALYTICS LIFECYCLE
DATA ANALYTICS LIFECYCLE
• Data science projects differ from BI projects
• More exploratory in nature
• Critical to have a project process
• Participants should be thorough and rigorous
• Break large projects into smaller pieces
• Spend time to plan and scope the work
• Documenting adds rigor and credibility
DATA ANALYTICS LIFECYCLE
• Data Analytics Lifecycle Overview
• Phase 1: Discovery
• Phase 2: Data Preparation
• Phase 3: Model Planning
• Phase 4: Model Building
• Phase 5: Communicate Results
• Phase 6: Operationalize
• Case Study: GINA
2.1 DATA ANALYTICS
LIFECYCLE OVERVIEW
• The data analytic lifecycle is designed for Big Data problems and
data science projects
• With six phases the project work can occur in several phases
simultaneously
• The cycle is iterative to portray a real project
• Work can return to earlier phases as new information is uncovered
2.1.1 KEY ROLES FOR A
SUCCESSFUL ANALYTICS
PROJECT
KEY ROLES FOR A
SUCCESSFUL ANALYTICS
PROJECT
• Business User – understands the domain area
• Project Sponsor – provides requirements
• Project Manager – ensures meeting objectives
• Business Intelligence Analyst – provides business domain
expertise based on deep understanding of the data
• Database Administrator (DBA) – creates DB environment
• Data Engineer – provides technical skills, assists data
management and extraction, supports analytic sandbox
• Data Scientist – provides analytic techniques and modeling
2.1.2 BACKGROUND AND OVERVIEW
OF DATA ANALYTICS LIFECYCLE
• Data Analytics Lifecycle defines the analytics process and
best practices from discovery to project completion
• The Lifecycle employs aspects of
• Scientific method
• Cross Industry Standard Process for Data Mining (CRISP-DM)
• Process model for data mining
• Davenport’s DELTA framework
• Hubbard’s Applied Information Economics (AIE) approach
• MAD Skills: New Analysis Practices for Big Data by Cohen et al.
https://en.wikipedia.org/wiki/Scientific_method
https://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining
http://www.informationweek.com/software/information-management/analytics-at-work-qanda-with-tom-davenport/d/d-id/1085869?
https://en.wikipedia.org/wiki/Applied_information_economics
https://pafnuty.wordpress.com/2013/03/15/reading-log-mad-skills-new-analysis-practices-for-big-data-cohen/
OVERVIEW OF
DATA ANALYTICS LIFECYCLE
2.2 PHASE 1: DISCOVERY
2.2 PHASE 1: DISCOVERY
1. Learning the Business Domain
2. Resources
3. Framing the Problem
4. Identifying Key Stakeholders
5. Interviewing the Analytics Sponsor
6. Developing Initial Hypotheses
7. Identifying Potential Data Sources
2.3 PHASE 2: DATA PREPARATION
2.3 PHASE 2: DATA
PREPARATION
• Includes steps to explore, preprocess, and condition
data
• Create robust environment – analytics sandbox
• Data preparation tends to be t.
Oracle NoSQL DB & InfiniteGraph - Trends in Big Data and Graph TechnologyInfiniteGraph
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1) There is a very long feedback cycle when developing workable software applications due to the time needed to wait for quality data.
2) Switching costs between tasks and projects are high for big data applications.
3) Releasing features to all users can result in more questions about data quality issues.
4) Successfully developing workable big data applications has a very low success rate and wastes many resources.
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Keynote: Graphs in Government_Lance Walter, CMONeo4j
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The first step towards understanding what data assets mean for your organization is understanding what those assets mean for each other. Metadata—literally, data about data—is one of many data management disciplines inherent in good systems development, and is perhaps the most mislabeled and misunderstood out of the lot. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight, the efficiency of organizational practices, and can also enable you to combine more sophisticated data management techniques in support of larger and more complex business initiatives.
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Illustrate how to leverage metadata in support of your business strategy
Discuss foundational metadata concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBOK)
Enumerate guiding principles for and lessons previously learned from metadata and its practical uses
Unlocking New Insights with Information DiscoveryAlithya
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Every year the State of Texas CIO releases the five-year State Strategic Plan with IT initiatives for government organizations to implement. How many of the items from the November 2014 plan update have you planned for or put in place? If you need help aligning with these state objectives, join this session to learn how ER/Studio can enhance your data architecture to meet these goals.
In this age of data policies and protection, Texas State agencies are required to develop controls to ensure confidentiality, integrity and availability of their data. In this webinar, we’ll show a live demonstration of ER/Studio and describe how it addresses key areas of the strategic objectives, including:
+ Data security and privacy classifications
+ Data quality and availability requirements
+ Enterprise planning and collaboration within and across organizations
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Enterprise ready: a look at Neo4j in production
1. 6/28/2017
1
Enterprise Ready: Neo4j in Production
#1 Database for Connected Data
Jeff Morris
Head of Product
Marketing
6/27/17
Enterprise Ready
Neo4j in Production
June 2017
2. 6/28/2017
2
Who We Are: The Graph Database for Connected Data
Neo4j is an enterprise-grade native graph database that enables you to:
• Store and query data relationships
• Traverse any levels of depth on real-time
• Add and connect new data on the fly
• Performance
• ACID Transactions
• Agility
3
Designed, built and tested natively
for graphs from the start to ensure:
• Developer Productivity
• Hardware Efficiency
Graph is Top Trending Database Type
4. 6/28/2017
4
Background
• SF-based C2C rental platform
• Dataportal democratizes data access for
growing number of employees while improving
discoverability and trust
• Data strewn everywhere—in silos, in segmented
departments, nothing was universally accessible
Business Problem
• Data-driven culture hampered by variety and
dependability of data, tribal knowledge and
word-of-mouth distribution
• Needed visibility into information usage, context,
lineage and popularity across company of 3,000+
Solution and Benefits
• Offers search with context & metadata, user &
team-centric pages for origin & lineage
• Nodes are resources: data tables, dashboards,
reports, users, teams, business outcomes, etc.
• Relationships reflect consumption, production,
association, etc.
• Neo4j, Elasticsearch, Python
Airbnb Dataportal TRAVEL TECHNOLOGY
Knowledge Graph, Metadata Management8
CE users since 2017
Background
• Large global bank
• Deploying Reference Data to users and systems
• 12 data domains, 18 datasets, 400+ integrations
• Complex data management infrastructure
Business Problem
• Master data silos were inflexible and hard to
consume
• Needed simplification to reduce redundancy
• Reduce risk when data is in consumers’ hands
• Dramatically improve efficiency
Solution and Benefits
• Data distribution flows improved dramatically
• Knowledge Base improves consumer access
• Ad-hoc analytics improved
• Governance, lineage and trust improved
• Better service level from IT to data consumers
UBS FINANCIAL SERVICES
Master Data Management / Metadata9
CE Customer since 2016 Q1EE Customer since 2015
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5
Background
• 5 year long drug discovery research
• Parse & Navigate over 25 Million scientific papers
• Sourced from National Library of Research and
tagging of “Medical Subject Headers” (MeSH tags)
Business Problem
• Seeking to automate phenotype, compound and
protein cell behavior research by using previously
documented research more effectively
• Text mining for research elements like DNA strings,
proteins, RNA, chemicals and diseases
Solution and Benefits
• Found ways to identify compound interaction
behavior from millions of research documents
• Relations between biological entities can be
identified and validated by biologic experts
• Still very challenging to keep up-to-date, add
genomics data, and find a breakthrough
Novartis PHARMACEUTICAL RESEARCH
Content Management / Biomedical Research10
CE Customer since 2016 Q1CE Customer since 2012
Background
• How Neo4j is used in investigations
• Non-technical reporters manually gather data
• “Low-tech” data curation
• Journalists want to model data as a story, not
as data
Business Problem
• Identify repeated business relationships among
individuals and their holdings and accounts
• Scan documents and identify possible entities,
then create relationships between people and
documents.
• Names and alias variances
Solution and Benefits
• Uses Neo4j in “story discovery” phase
• Uncovers shortest paths for leads for reporters
• Many investigations underway now
Columbia University EDUCATION
Investigative Journalism / Fraud Detection11
CE Customer since 2016 Q1EE Customer since 2015 Q4
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6
Background
• eBay Israel, Entity Management Platform
• Taxonomy for hundreds of thousands of entities like
categories, products, sellers, sales, buyers, stores,
etc.
• Entities have permanent “souls” and “states” in
which they exist throughout their lifecycle
• All to make editing product items easy and fast
Business Problem
• Users demand high interactivity isolated
workspaces with inheritance for building pages
• Support versioning of entities so that users can
easily make changes, while preserving history of
its previous states
Solution and Benefits
• Chose Neo4j for performance, flexibility,
developer productivity
• Easy to learn
• Flexible way to represent how data entities
change throughout their lifecycle
• Patent pending
eBay Israel ONLINE RETAILER
Master Data Management / Metadata12
CE Customer since 2016 Q1EE Customer since 2015 Q4
Background
• French Telecom
• Big Data Governance in support for GDPR
• Environment with Hadoop, Analytics,
Recommendation engines, etc.
Business Problem
• Manage people, roles & rights, flow, audit, log
management, processes, policies, lineage,
metadata, lifecycles, security, etc…
• All because GDPR arrives in May 2018
Solution and Benefits
• Governance system oversees all systems
• Enforces correct policies
• Allows flexibility beyond Hadoop
• Architect has written Neo4j French manual
ORANGE TELECOMMUNICATIONS
Master Data Management / Metadata13
CE Customer since 2016 Q1EE Customer since 2015
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7
Neo4j in the Enterprise
Native Graph Differentiation
Graph Overview
CAR
name: “Dan”
born: May 29, 1970
twitter: “@dan”
name: “Ann”
born: Dec 5, 1975
since:
Jan 10, 2011
brand: “Volvo”
model: “V70”
Neo4j Invented the Labeled Property Graph Model
Nodes
• Can have name-value properties
• Can have Labels to classify nodes
Relationships
• Relate nodes by type and direction
• Can have name-value properties
MARRIED TO
LIVES WITH
PERSON PERSON
15
Neo4j Advantage - Agility
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8
Cypher: Powerful and Expressive Query Language
MATCH (:Person { name:“Dan”} ) -[:MARRIED_TO]-> (spouse)
MARRIED_TO
Dan Ann
NODE RELATIONSHIP TYPE
LABEL PROPERTY VARIABLE
Neo4j Advantage – Developer productivity
17
Example HR Query in SQL The Same Query using Cypher
MATCH (boss)-[:MANAGES*0..3]->(sub),
(sub)-[:MANAGES*1..3]->(report)
WHERE boss.name = “John Doe”
RETURN sub.name AS Subordinate,
count(report) AS Total
Project Impact
Less time writing queries
• More time understanding the answers
• Leaving time to ask the next question
Less time debugging queries:
• More time writing the next piece of code
• Improved quality of overall code base
Code that’s easier to read:
• Faster ramp-up for new project members
• Improved maintainability & troubleshooting
Productivity Gains with Graph Query Language
The query asks: “Find all direct reports and how many people they manage, up to three levels down”
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Open Source
(Available to anyone)
Apache 2.0
Open Source
(As part of Neo4j)
GPL v3
Open Process
(Open to anyone)
CIR, CIP, oCIM
Formal Standard
(Standards Body)
e.g. ANSI, ISO
openCypher
Documentation, TCK, Grammar, Parser
Opening the Language
Databases
Tools
ruruki
Vendor Support & Interest
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12
One more thing…
RDBMS Vocabulary Mapped to Graph Modeling
Relational DB Construct Graph DB Construct
Entity table Node labels
Row Node
Columns Node properties
Technical primary keys Replace with business primary keys
Constraints Unique constraints for business keys
Indexes Indexes on any property
Foreign keys Relationships
Default values Node keys
De-normalized or duplicated data Create separate nodes
Join tables Relationships
Join table columns Relationship properties
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13
Relational DBMSs Can’t Handle Relationships Well
• Cannot model or store data and relationships
without complexity
• Performance degrades with number and levels
of relationships, and database size
• Query complexity grows with need for JOINs
• Adding new types of data and relationships
requires schema redesign, increasing time to
market
… making traditional databases inappropriate
when data relationships are valuable in real-time
Slow development
Poor performance
Low scalability
Hard to maintain
Queries can take non-sequential,
arbitrary paths through data
Real-time queries need speed and
consistent response times
Queries must run reliably
with consistent results
Q
A single query can
touch a lot of data
Relationship Queries Strain Traditional Databases
2
7
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At Write Time:
data is connected
as it is stored
At Read Time:
Lightning-fast retrieval of data and relationships via
pointer chasing
Index free adjacency
Graph Optimized Memory & Storage
Neo4j: Native Graph from the Start
Native graph storage
Optimized for real-time reads and ACID writes
• Relationships stored as physical objects,
eliminating need for joins and join tables
• Nodes connected at write time, enabling
scale-independent response times
Native graph querying
Memory structures and algorithms optimized for graphs
• Index-free adjacency enables 1M+ hops per second via in-
memory pointer chasing
• Off-heap page cache improves operational robustness
and scaling compared with JVM-based caches
• “Minutes to milliseconds” performance improvement
Neo4j Advantage - Performance Neo4j Advantage - ACID Transactions
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Connectedness and Size of Data Set
ResponseTime
Relational and Other
NoSQL Databases
0 to 2 hops
0 to 3 degrees
Thousands of connections
1000x
Advantage
Tens to hundreds of hops
Thousands of degrees
Billions of connections
Graph
“Minutes to
milliseconds”
“Minutes to Milliseconds” Real-Time Query Performance
Equivalent Cypher Query
MATCH (you)-[:BOUGHT]->(something)<-[:BOUGHT]-(other)-[:BOUGHT]->(reco)
WHERE id(you)={id}
RETURN reco
Traversal Speeds on Amazon Retail Dataset
Threads Hops per second
1 3-4 million
10 17-29 million
20 34-50 million
30 36-60 million
3
1
Social Recommendation Example
Neo4j Advantage - Performance
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Graph databases are designed for data relationships
Discrete Data
Minimally
connected data
Fit for Purpose: The Right Architecture for the Right Job
Other NoSQL Relational DBMS Graph DB
Connected Data
Focused on
Data Relationships
Development Benefits
Easy model maintenance
Easy query
Deployment Benefits
Ultra high performance
Minimal resource usage
Graph
Graph DatabaseRDBMS
TabularAggregate Oriented (3)
Key-Value, Column-Family,
Document Database
Source: Martin Fowler NoSQL Distilled
Database Management Systems
Five Key Sub-Patterns (incl. SQL)
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NoSQL Databases Don’t Handle Relationships
• No data structures to model or store
relationships
• No query constructs to support data
relationships
• Relating data requires “JOIN logic”
in the application
• No ACID support for transactions
… making NoSQL databases inappropriate when
data relationships are valuable in real-time
UNIFIED, IN-MEMORY MAP
Lightning-fast
queries due to
replicated in-memory
architecture and
index-free adjacency
MACHINE 1 MACHINE 2 MACHINE 3
Slow queries
due to
index lookups +
network hops
Using Graph
Using Other NoSQL to Join Data
Q R
Q R
Relationship Queries on non-native Graph Architectures
3
5
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Neo4j Scalability
Dynamic pointer compression
Unlimited-sized graphs with no
performance compromise
Index partitioning
Auto-partitioning of indexes into
2GB partitions
Causal clustering architecture
Enables unlimited read scaling
with ACID writes and a choice
of consistency levels
Multi-Data Center Support
Creates HA, Fault Tolerant Global
Applications
Efficient processing
Native graph processing and storage
often requires 10x less hardware
Efficient storage
One-tenth the disk and memory
requirements of certain alternatives
Neo4j Advantage – Scalability
Neo4j Performance Improvements by Version
0
2000
4000
6000
8000
10000
12000
14000
Neo4j 2.2 Neo4j 2.3 Neo4j 3.0 Neo4j 3.1 Neo4j 3.2
Complex Mixed-Workload Throughput
32%
50%
27%
70%
320%
Faster
than 2.2
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Raft-based architecture
• Continuously available
• Consensus commits
• Third-generation cluster architecture
Cluster-aware stack
• Seamless integration among drivers,
Bolt protocol and cluster
• No need for external load balancer
• Stateful, cluster-aware sessions with
encrypted connections
Streamlined development
• Relieves developers from complex infrastructure concerns
• Faster and easier to develop distributed graph applications
Neo4j Enterprise: Causal Clustering Architecture
Modern and Fault-Tolerant to Guarantee Graph Safety
38
Neo4j Advantage – Scalability
Global Cluster
Topologies
Geo Aware
Load Balancing
Tiered Replicas
Full-Stack API
US EAST GROUP
UK GROUP
HK GROUP
SA GROUP
Now in Neo4j 3.2: Multi-DC Clustering
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How Causally Consistent Reads Work
App ServerApp Server
DriverDriver
3:
Review
Profile
4:
Create
an order
Async
Replication
Raft
Replication
1: Read
Product
Catalog
Core
Server
Core
Server
Replica
Server
App ServerApp Server
DriverDriver
App ServerApp Server
DriverDriver
ENTERPRISE
EDITION
2. Create
Account
5:
Review
orders
How it Works:
• Application chooses a consistency level
“Read Any” vs “Read your own writes”
• Cluster chooses appropriate members
Default optimizes for scalability
(i.e. read replica server for reads)
Causal Clustering Enables:
• Application-driven SLAs
• Optimizing for freshness vs. cost
• Tunability within an application
On an application & session basis
1: Read any replica | 2: Write [Tx 101] | 3: RYOW*[Tx 101] | 4: Write [Tx 102] | 5: RYOW [Tx 102]
Graph Transactions Over
ACID Consistency
Graph Transactions Over
Non-ACID DBMSs
41
Maintains Integrity Over Time Eventual Consistency Becomes Corrupt Over Time
The Importance of ACID Graph Writes
• Ghost vertices
• Stale indexes
• Half-edges
• Uni-directed ghost edges
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Summary of Neo4j: Built for the Enterprise
Native Graph Storage
Designed, built, and tested for graphs
Native Graph Query Processing
For real-time, relationship-based apps
Evaluate millions of relationships in a blink
Whiteboard-Friendly Data Modeling
Faster projects compared to RDBMS
Data Integrity and Security
Fully ACID transactions, causal consistency
and enterprise security
Powerful, Expressive Query Language
Improved productivity, with 10x to 100x
less code than SQL
Scalability and High Availability
Architecture provides ideal balance of
performance, availability, scale for graphs
Built-in ETL
Seamless import from other databases
Integration
Fits easily into your IT environment, with
drivers and APIs for popular languages
MATCH
(A)42
Case Studies for Knowledge Graphs
and Recommendation Engines
Neo4j Case Studies
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Sys Admins
Servers, on-premise virtual machines,
cloud virtual machines, etc.
Network Admins
Switches, Routers, Egress Points
App Admins
I.e. Salesforce, Marketo, SAP, Oracle
Apps, Tableau, SharePoint, DBA’s etc.
Internal Users
HR, Sales, Marketing, Data Analysts,
E-staff etc.
Numerous Customers & Partners
Router
Servers
Servers
Apps
FirewallCloud
Switch
Apps
Network Admins
Switches, Routers, Egress Points
Sys Admins
Servers, on-premise virtual machines,
cloud virtual machines, etc.
App Admins
I.e. Salesforce, Marketo, SAP, Oracle
Apps, Tableau, SharePoint, DBA’s etc.
Internal Users
HR, Sales, Marketing, Data Analysts,
E-staff etc.
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25
Router
Servers
Servers
Apps
FirewallCloud
Switch
Apps
Network Admins
Switches, Routers, Egress Points
Sys Admins
Servers, on-premise virtual machines,
cloud virtual machines, etc.
App Admins
I.e. Salesforce, Marketo, SAP, Oracle
Apps, Tableau, SharePoint, DBA’s etc.
Internal Users
HR, Sales, Marketing, Data Analysts,
E-staff etc.
Router
Servers
Servers
Apps
FirewallCloud
Switch
Apps
Network Admins
Switches, Routers, Egress Points
Sys Admins
Servers, on-premise virtual machines,
cloud virtual machines, etc.
App Admins
I.e. Salesforce, Marketo, SAP, Oracle
Apps, Tableau, SharePoint, DBA’s etc.
Internal Users
HR, Sales, Marketing, Data Analysts,
E-staff etc.
26. 6/28/2017
26
Router
Servers
Servers
Apps
FirewallCloud
Switch
Apps
Network Admins
Switches, Routers, Egress Points
Sys Admins
Servers, on-premise virtual machines,
cloud virtual machines, etc.
App Admins
I.e. Salesforce, Marketo, SAP, Oracle
Apps, Tableau, SharePoint, DBA’s etc.
Internal Users
HR, Sales, Marketing, Data Analysts,
E-staff etc.
Router
Servers
Servers
Apps
FirewallCloud
Switch
Apps
Network Admins
Switches, Routers, Egress Points
Sys Admins
Servers, on-premise virtual machines,
cloud virtual machines, etc.
App Admins
I.e. Salesforce, Marketo, SAP, Oracle
Apps, Tableau, SharePoint, DBA’s etc.
Internal Users
HR, Sales, Marketing, Data Analysts,
E-staff etc.
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Background
• Large Nordic Telecom Provider
• 1M Broadband routers deployed in Sweden
• Half of subscribership are over 55yrs old
• Each household connects 10 devices
• Goal to improve customer experience
Business Problem
• Broadband router enhancement to improve
customer experience
• Context-based in home services
• How to build smart home platform that allows
vendors to build new “home-centric” apps
Solution and Benefits
• New Features deployed to 1M homes
• API-based platform for easy apps that:
• Automatically assemble Spotify playlists
based on who is in the house
• Notify parents when children get home
• Build smart shopping lists
TELIA ZONE TELECOMMUNICATIONS
Smart Home / Internet of Things55
EE Customer since 2016 Q4
Background
• Large Public University – “U-Dub”
• IT staff for 80K+ students and employees
• Transforming IT systems from mainframe to cloud
• Providing IT & data warehousing services to 3
campuses, 6 hospitals, and 6,300 EDW users
Business Problem
• Old Sharepoint metadata was too complicated
for users, not flexible and not transparent
• $1B project to migrate HR system from
mainframe to Workday needed to be smooth
• Future projects needed repeatable predictability
• Needed new glossary, impact analysis, analytics
Solution and Benefits
• Consulted with NDU peers, built simple model
• Built Visualizer with Elasticsearch, Neo4j & D3.js
• Improved predictability, lineage, and impact
understanding for over 6,300 users
University of Washington EDUCATION & RESEARCH
Metadata Management, IT & Network Operations56
CE Customer since 2016 Q1
28. 6/28/2017
28
Background
• Ad-Tech supplier in NYC identifies "intent signals"
• Collects device-born consumer data from mobile,
desktops & tablets
• Contains device and buyer data on more than
90% of American households
• Supersized Graph
Business Problem
• Recognize buyer receptivity to offers near time of
purchase
• Device data and consumer behaviors change
frequently
• Triangulate who is holding a device, where and
when it happens, to signal active purchase intent,
and create real-time offers to assist user
Solution and Benefits
• 3 Billion nodes, 9 billion relationships
• 1 Billion daily transactions on 3 servers
• Hybrid solution with Neo4j, Hadoop, Spark,
MongoDB and Ruby
• Breakthrough results from 60%-250%
higher than industry benchmarks
Qualia ADVERTISING TECHNOLOGY
Social Network, Internet of Things, and Real-Time Buyer Identification57
EE Customer since 2014 Q3
Background
• World's largest hospitality / hotel company
• 7th largest web site on internet
• 1.5 M hotel rooms offered online by 2018
• Revenue Management System that allows
property managers to update their pricing rates
Business Problem
• Provide the right room & price at the right time
• Old rate program was inflexible and bogged down
as they increased the pricing options per property
per day
• Lay the path to be an innovator in the future
Solution and Benefits
• 2016-era rate program embeds Neo4j as "cache"
• Created a graph per hotel for 4500 properties in 3
clusters
• 1000% increase in volume over 4 years
• 50% decrease in infrastructure costs
• "Use Neo4j Support!"
MARRIOTT TRAVEL & HOSPITALITY SERVICES
Pricing Recommendations Engine58
EE Customer since 2014 Q2
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29
Background
• Personal shopping assistant
• Converses with buyer via text, picture and voice
to provide real-time recommendations
• Combines AI and natural language understanding
(NLU) in Neo4j Knowledge Graph
• First of many apps in eBay's AI Platform
Business Problem
• Improve personal context in online shopping
• Transform buyer-provided context into ideal
purchase recommendations over social platforms
• "Feels like talking to a friend"
Solution and Benefits
• 3 developers, 8M nodes, 20M relationships
• Needed high-performance traversals to respond
to live customer requests
• Easy to train new algorithms and grow model
• Generating revenue since launch
eBay ShopBot ONLINE RETAIL
Knowledge Graph powers Real-Time Recommendations59
EE Customer since 2016 Q3
Case Study: Knowledge Graphs at eBay
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33
Enterprise or Community Edition
Summary
Enterprise-Class Technology
Ready for real-time enterprise applications
Performance and Scalability
• Clustered replication across
data centers
• Unlimited graph sizes
• Intelligent online space reuse
• Enterprise lock manager
• Compiled runtime for common
queries
• Kerberos authentication add-on
• Clustering on CAPI flash add-on
Monitoring and Administration
• Advanced monitoring by role
• Cypher query tracing
• Hot backups
• Enterprise security
Enterprise Schema Governance
• Property existence constraints
• Composite and node key constraints
68
34. 6/28/2017
34
Features in Community and Enterprise Editions
69
Both Editions—GRAPH Features Database Features Architecture Features
Labeled Property Graph Model ACID Transactions Language drivers for Java, Python, C# & JavaScript
Native Graph Processing & Storage High-performance Native API HTTPS plug-in
Graph Query Language “Cypher” High-performance caching REST API
Neo4j Browser w/ Syntax Highlighting Cost-based query optimizer RPM, Azure & AWS Cloud Delivery
Fast Writes via Native Label Index
Fast Reads via Composite Indexes
Enterprise Edition—GRAPH Features Database Features Architecture Features
Database storage reallocation Query monitoring with enriched metrics Enterprise Lock Manger accesses all available cores on server
Cypher query tracing
Compiled Cypher Runtime to
accelerate common queries
Causal Clustering, core and read-replica design
Node Key schema constraints User & role-based security Multi-Data Center Support for global scale
Property existence constraints LDAP & Active Directory Integration Driver-based load balancing
Kerberos Security plug-in Driver-based Causal Clustering API exposes routing logic
Bold is new in 3.2
Licensing Options
70
Edition / Program Audience License Price Point
Community Edition IT Developers GPLv3 Free
Enterprise Edition
Fair Trade
Projects
AGPL3 Free, but must publish source code
Enterprise Edition
Real-time
applications
Commercial ~$500/month/core
Early Startups
Early Stage, <20
employees
Commercial Free until traction established
Startups w/ Traction <3M ARR Commercial $1,500/month
Most deployments require only 3 server cluster for fault tolerance & HA
35. 6/28/2017
35
Enterprise-Class Expertise
Neo4j Customer Success
Expert design, development and
deployment services
• Graph and application design
• Application deployment
• Data center configuration
• Developer and user training
• World-class support with SLAs
• Support portal and knowledge base
Graph Innovation Network
Worldwide community of Neo4j and
graph database experts
• Service providers
• OEMs and VARs
• Technology partners
• Open source community
Use Neo4j experts and join the Innovation Network.
Develop your apps right the first time.
71
The Largest Graph Innovation Network
3,000,000+ with 50k additional per month
Neo4j Downloads
3,000,000+ with 50k additional per month
Neo4j Downloads
225+ customers
50% from Global 2000
225+ customers
50% from Global 2000
100+
Technology and Services Partners
100+
Technology and Services Partners
450+ annual events & 10k attendees
Graph and Neo4j awareness and training
450+ annual events & 10k attendees
Graph and Neo4j awareness and training
43,000+
Neo4j Meetup Members
43,000+
Neo4j Meetup Members
50,000+
Online and Classroom Education Registrants
50,000+
Online and Classroom Education Registrants
36. 6/28/2017
36
Users Love Neo4j
Graph Visionaries
Enterprise Customers
Graph Visionaries
Enterprise Customers
74
Partners
System Integrators
Trainers
OEMs
Partners
System Integrators
Trainers
OEMs
Cloud
IaaS, PaaSm, DBaaS
Marketplace
Cloud
IaaS, PaaSm, DBaaS
Marketplace
OSS
Community
Events
Forums
Add-Ons
The Density of the Neo4j Innovation Network
Tech
Ecosystem
OEM & Tech
Partners
Tech
Ecosystem
OEM & Tech
Partners
Graph Solutions
Data Science
Architecture
Data Models
Graph Solutions
Data Science
Architecture
Data Models
Commercial
Support
Technical Support
Packaged Services
Custom Services
Commercial
Support
Technical Support
Packaged Services
Custom Services
Education
Documents
Online Training
Classroom
Custom Onsite
Education
Documents
Online Training
Classroom
Custom Onsite
Standards
Initiatives
openCypher,
LDBS
Standards
Initiatives
openCypher,
LDBS
37. 6/28/2017
37
The Connected Enterprise Value Proposition
Fastest path to Graph Success
Graph
Expertise
Graph
Expertise
Graph
Database
Platform
Graph
Database
Platform
Innovation
Network
Innovation
Network
Enterprise-Grade
Innovation Launchpad
• Neo4j Enterprise Edition
• HA, Causal Cluster, MDC
• Better performance
• Hardened product
The Next Innovation
• Density of the network accelerates
innovation opportunity
• Thousands of project successes
• Partners, Service Providers,
Vendors, Academics, Researchers
Millions of Graph Hours
• Shrink learning curve
• Design advice
• Contextual experience
• Deploy & Ops support
75
Neo4j
Commercial
Value
Analysts are Invited to Attend GraphConnect NYC
76