5. Data Management in 1979
Paper Forms
Tiny RAM Spinning Platters
(Low Capacity /
Slow, Sequential IO) RDBMS
Relational Model
The RDBMS Era
Confidential - Neo4j, Inc.
6. Data Management Today
Dynamic Real-World Systems
Abundant
RAM
Flash & IO Co-
Processors
(High-Capacity Storage &
Ultra-Fast Random I/O)
Confidential - Neo4j, Inc.
A New Graph Era Emerging
Neo4j
Property Graph Model
Real-Time
Connected Data
9. 9
TRADITIONAL
DATABASES
BIG DATA
TECHNOLOGY
Store and retrieve data Aggregate and filter data
Real time storage & retrieval
Long running queries
Aggregation & filtering
Up to
3
Max #
of
hops
1
IT Portfolio Perspective
10. 10
TRADITIONAL
DATABASES
BIG DATA
TECHNOLOGY
Store and retrieve data Aggregate and filter data Connections in data
Real time storage & retrieval Real-Time Connected Insights
Long running queries
Aggregation & filtering
“Our Neo4j solution is literally thousands of times faster
than the prior MySQL solution, with queries that require
10-100 times less code”
Volker Pacher, Senior Developer
Up to
3
Max #
of
hops
1 Millions
IT Portfolio Perspective
11. Illustration by David Somerville based on the original by Hugh McLeod (@gapingvoid)
RDBMS
&
Aggregate-
Oriented NoSQL
Hadoop /
EDW/
Columnar
RDBMS
|<———————- Graph Database & ———————>|
Graph Compute Engine
(Graph Transactions & Analytics)
12. 3. A Technical Architecture Perspective
Core Technology Differences
14. 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
Neo4j
“Minutes to
milliseconds”
This Enables:
“Minutes to Milliseconds” Real-Time Query Performance
15. ACID Consistency Non ‘Graph-ACID’ DBMSs
15
Maintains Integrity Over Time
Guaranteed Graph Consistency
Becomes Corrupt Over Time
Not ‘Good Enough’ for Graphs
And is Supported By:
ACID Graph Writes : A Requirement for Graph Transactions
16. What Is Different In Neo4j?
Cypher Query Language
16
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
17. 17
Neo4j Graph Database: Foundational Components
1
2
3
4
5
6
Index-Free Adjacency
In memory and on flash/disk
vs
ACID Foundation
Required for safe writes
Full-Stack Clustering
Causal consistency
Language, Drivers, Tooling
Developer Experience,
Graph Efficiency, Type Safety
Graph Engine
Cost-Based Optimizer, Graph
Statistics, Cypher Runtime
Hardware Optimizations
For next-gen infrastructure
18. Neo4j Graph Database: Enterprise Infrastructure
18
Neo4j Security Foundation Multi-Clustering Support for
Global Internet Apps
Rolling Upgrades
Schema Constraints Concurrent/Transactional Write
Performance
Auto Cache Reheating
For Restarts, Restores and Cluster
Expansion
Neo4j 3.4 now supports
rolling upgrades
3.4 3.5
Upgrade older instances while keeping other
members stable and without requiring a restart
of the environment
3.5
23. 23
The Neo4j Desktop Platform
Graph Apps for Technologies & End Users
The easiest way to start building Neo4j apps
Includes Neo4j Enterprise for Development, Neo4j Browser, APOC, Graph Algorithms, and more
https://neo4j.com/download
25. 25
Graph Visualization Options with Neo4j
Neo4j Bloom
Provided by Neo4j
Exclusively optimized for Neo4j
graphs
Deploys easily in Neo4j Desktop
Focused on graph exploration
thru a code-free UI
Near natural language search
Currently caters to data
analysts and graph SMEs
Currently for individual or small
team use
Viz Toolkits
3rd party e.g. vis.js, d3.js, Keylines
Some offer data hooks into
Neo4j, others may require
custom integration
Offer robust APIs for flexible
control of the viz output
Cater to developers who will
create a custom solution, usually
with limited interactivity
Departmental, enterprise or
public use
BI Tools
3rd party e.g. Tableau, Qlik
Not optimized for graph data,
may require a special connector
UI for dashboard and report
creation with many kinds of viz,
in addition to graph viz
Cater to business users and
data analysts
Departmental, cross-
department or enterprise use
Graph Viz Solutions
3rd party, incl. Kineviz,
Graphistry, Linkurious, …
Have to support multiple
graph models and sources
Feature UI for exploration or
APIs for customizing output
and embedding/publishing
Solutions may cater to
business users, analysts or
developers
Small team, departmental or
cross-department use
Little technical expertise Most technically involved
Exploration focused Publishing / Consumption focused
Smaller deployments Larger deployments
26. Graph Analytics:
Graph & ML Algorithms
neo4j.com/
graph-algorithms-
book/
Pathfinding
& Search
Centrality /
Importance
Community
Detection
Link
Prediction
Finds optimal paths
or evaluates route
availability and quality
Determines the
importance of distinct
nodes in the network
Detects group
clustering or partition
options
Evaluates how
alike nodes are
Estimates the likelihood
of nodes forming a
future relationship
Similarity
29. Language Standardization
Working towards industry agreement across vendors
29
ISO GQLopenCypher
Supporting Cypher as an
industry-shared language
(since 2015)
Evolving towards a formal
language Standard
https://openCypher.org
