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Beyond Big Data: Leverage
Large-Scale Connections
and IBM Power Systems
October 4th, 2017
1. Why graph technologies?
2. How enterprises are using 

native-graph
3. Common Neo4j 

reference architecture
4. Neo4j o...
3
4
Hierarchies
On Stage
Business
Processes
Behind the Scene
Data
Structure
Linear Supply Chain Information
On Stage
Behind the Scene
Linear Supply Chain InformationOrganizations Multi-related Knowledge
Business
Processes
Data
Str...
”Graph analysis is possibly the single most effective
competitive differentiator for organizations pursuing
data-driven op...
Connected Data is Transforming Industries
Social Graph
Knows
Knows
Knows
Knows
People & Products
Bought
Bought
Viewed
Retu...
Why Native-Graph?
Native Property Graph
The Whiteboard Model is
the Physical Model
A unified view for
ultimate agility
• Easily understood
• ...
Property Graph Model Components
Nodes
• Can have name-value properties
• Can have Labels to classify nodes
• Relate nodes ...
Store / Retrieve
Store / Retrieve
Store / Retrieve
Load Data
Store / Retrieve
Load Data
Store / Retrieve
Load Data
Store / Retrieve
Load Data
Actionable InsightsStore / Retrieve
RELATIONAL DB DOCUMENT STORE WIDE COLUMN STORE DOCUMENT STORE RELATIONAL DB KEY VALUE STORE
Leveraging Cross-Silo Connecti...
About Neo4j
• Neo4j powers the next generation of
applications and analytics
• Prominent use cases are found in areas
like machine lea...
C
34,3%B
38,4%A
3,3%
D
3,8%
1,8%
1,8%
1,8%
1,8%
1,8%
E
8,1%
F
3,9%
Static world Connected World
Native Graph Platform
Neo4...
Ecosystem
Neo4j Professional Services
300+ partners
47,000 group members
61,000 trained engineers
3.5M downloads
Mindset
“...
Digital native companies like Medium, Ebay, and
LinkedIn, as well as companies in transformation
like Walmart, Adidas and ...
Hundreds of successful deployments
— from Fortune 500 companies to exciting startups
Examples of enterprise adoption:
Adoption Highlights
Retail
7 out of top 10
retailers in the world
Finance
12 out of 25 top
financial services firms
8 out of...
Case Studies
Real-Time
Recommendations
Fraud
Detection
Network &
IT Operations
Master Data
Management
Graph-Based
Search
Identity & Acc...
29
Ebay powers its machine
learning based ‘shopbot’
with Neo4j knowledge
graph
"Feels like talking to a friend"
”
Online S...
Ebay powers its machine
learning based ‘shopbot’
with Neo4j knowledge
graph
30
Online Shopping
• 3 developers, 8M nodes, 2...
UBS is using Neo4j to manage their complex data
infrastructure of over 400 integration points across 18 data-
domains to i...
“Neo4j’s high performance engine provides flexibility of
data representation along with features that go beyond
traditional...
Marriott is using Neo4j to allow hotel managers to
control room rate price optimization across 1.5 million
rooms on a dail...
Product Overview
ı
Neo4j: #1 Database for Connected Data
Neo4j is an enterprise-grade native graph database that enables you to:
• Store an...
Q R
Q R
Using Other NoSQL to Join Data
Using Neo4j
Slow queries due to
index lookups &
network hops
Lightning-fast queries...
Real-Time Query Performance
Relational and
Other NoSQL
Databases
ResponseTime
Connectedness and Size of Data Set
0 to 2 ho...
How Fast is Fast?
*6 machines, each with 48 VCPUs, 256 GB disk and 256 GB of RAM; ~10M node, ~100M relationship graph
Work...
Keeping Your Graph Intact Essential for Graph Operations
Atomic Causal Consistency
The graph
transaction
moves together
as...
Non-Native
Graph DB
Keeping Your Graph Intact Essential for Graph Operations
Atomic Causal Consistency Non-Atomic Eventual...
Neo4j Supported Platforms
On-Premise Platforms Cloud Platforms and Containers
IBM	POWER
For	Development
...and others
Native Graph Storage

Designed, built, and tested for graphs
Native Graph Query Processing

For real-time, relationship-ba...
Solution Architecture
CyberSecurity Example
43
Nav Mathur
Sr. Director Global Solutions @ Neo4j
@nav_mathur, in/navmathur
44
45
USE
ISSUES
Terminal ATM-
skimming
Data Breach
Card Holder
Card Issuer
Fraudster
USE $5MAKES
$10
MAKES
$2
MAKES
MAKES $4...
Money
Transferring
Purchases Bank
Services Relational
database
Develop Patterns
Data Science-team
+ Good for Discrete Anal...
Money
Transferring
Purchases Bank
Services Relational
database
Data Lake
+ Good for Map Reduce
+ Good for Analytical Workl...
Neo4j powers
360° view of
transactions in
real-time
Neo4j
Cluster
SENSE
Transaction
stream
RESPOND
Alerts &
notification
LO...
Business Value
Real-Time Fraud Prevention
• $MM recaptured in savings
• Increased revenue & customer
satisfaction due to d...
Your Engines 

& Algorithms
Customer
Facing Apps
Native Graph 

Database
Customer 

Data Sources
Complete Solution
Real-Ti...
51
ON
Neo4j On IBM Power8
Real - Time Graph Processing That’s
Entirely In-Memory
Richard Sheppard
Director of Sales @ Blai...
4X
Threads per core*
4X
Mem. Bandwidth*
4X
More cache* @
Lower Latency
These design decisions result in best performance f...
The POWER of an open ecosystem
ON
300Worldwide members of 2,500+
Linux ISVs developing on
POWER
30
Hardware and
technology...
innovations under way
POWER8 with CAPI enabled acceleration running Neo4j delivers 

1.61X the price-performance versus In...
Scalability
Only POWER8 can provide up to 56
terabytes of extended memory space
with up to 40 terabyte CAPI flash
architec...
56
➢ Trusted IBM Business Partner for 21 years
➢ Enabling business to utilize data for improved business insights
➢ Certif...
Next Steps
Register for a brown-bag graph talk with
your team @ https://neo4j.com/brownbag/
Attend GraphConnect use 50% of...
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Beyond Big Data: Leverage Large-Scale Connections

Today’s CIOs and CTOs don’t just need to manage larger volumes of data – they need to generate insight from their existing data. In this case, the relationships between data points matter more than the individual points themselves. In order to leverage data relationships, organizations need a database technology that stores relationship information as a first-class entity. That technology is a graph database.

Attend this webinar to hear about:
1. Why graph technologies are essential for the future of increasingly connected data
2. How enterprises such Walmart, eBay, and UBS are using Neo4j’s native-graph platform for a diverse set of use cases, including security & fraud detection, real-time recommendation engines, master data and many more
3. And how Neo4j on IBM POWER8 can scale your massive graph data with real-time graph processing that’s entirely in-memory.

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Beyond Big Data: Leverage Large-Scale Connections

  1. 1. Beyond Big Data: Leverage Large-Scale Connections and IBM Power Systems October 4th, 2017
  2. 2. 1. Why graph technologies? 2. How enterprises are using 
 native-graph 3. Common Neo4j 
 reference architecture 4. Neo4j on IBM POWER8 to 
 better leverage your big data Agenda Nav Mathur Sr. Director Global Solutions @ Neo4j @nav_mathur, in/navmathur Richard Sheppard Director of Sales @ Blair Technology Solutions in/rmjsheppard Amy Hodler Sr. Marketing Mgr @ Neo4j @amyhodler, in/amyhodler
  3. 3. 3
  4. 4. 4
  5. 5. Hierarchies On Stage Business Processes Behind the Scene Data Structure Linear Supply Chain Information
  6. 6. On Stage Behind the Scene Linear Supply Chain InformationOrganizations Multi-related Knowledge Business Processes Data Structure
  7. 7. ”Graph analysis is possibly the single most effective competitive differentiator for organizations pursuing data-driven operations and decisions“ The Impact of Graphs
  8. 8. Connected Data is Transforming Industries Social Graph Knows Knows Knows Knows People & Products Bought Bought Viewed Returned Bought People & Content adidas Plays Lives_in In_sport Likes Fan_of Plays_for
  9. 9. Why Native-Graph?
  10. 10. Native Property Graph The Whiteboard Model is the Physical Model A unified view for ultimate agility • Easily understood • Easily evolved • Easy collaboration between business and IT
  11. 11. Property Graph Model Components Nodes • Can have name-value properties • Can have Labels to classify nodes • Relate nodes by type and direction • can have name-value properties name:”Dan” born: May 29, 1970 twitter:”@dan” name:”Ann” born: Dec 5, 1975 Since: Jan 10, 2011 brand: “Volvo” model: “V70” CAR Married_to Lives_with D rives PERSON O wns Relationships PERSON
  12. 12. Store / Retrieve
  13. 13. Store / Retrieve
  14. 14. Store / Retrieve
  15. 15. Load Data Store / Retrieve
  16. 16. Load Data Store / Retrieve
  17. 17. Load Data Store / Retrieve
  18. 18. Load Data Actionable InsightsStore / Retrieve
  19. 19. RELATIONAL DB DOCUMENT STORE WIDE COLUMN STORE DOCUMENT STORE RELATIONAL DB KEY VALUE STORE Leveraging Cross-Silo Connections
  20. 20. About Neo4j
  21. 21. • Neo4j powers the next generation of applications and analytics • Prominent use cases are found in areas like machine learning, personalized recommendations, fraud detection, data governance and more. Neo4j: The #1 Platform for Connected Data
  22. 22. C 34,3%B 38,4%A 3,3% D 3,8% 1,8% 1,8% 1,8% 1,8% 1,8% E 8,1% F 3,9% Static world Connected World Native Graph Platform Neo4j is an internet-scale, native graph database which executes connected workloads faster than any other database management system. Neo4j
  23. 23. Ecosystem Neo4j Professional Services 300+ partners 47,000 group members 61,000 trained engineers 3.5M downloads Mindset “Graph Thinking” is all about considering connections in data as important as the data itself. Native Graph Platform Neo4j is an internet-scale, native graph database which executes connected workloads faster than any other database management system. Neo4j
  24. 24. Digital native companies like Medium, Ebay, and LinkedIn, as well as companies in transformation like Walmart, Adidas and Airbus, have all chosen to adopt Neo4j.
  25. 25. Hundreds of successful deployments — from Fortune 500 companies to exciting startups Examples of enterprise adoption:
  26. 26. Adoption Highlights Retail 7 out of top 10 retailers in the world Finance 12 out of 25 top financial services firms 8 out of top 10 software vendors Software (As per 2017)
  27. 27. Case Studies
  28. 28. Real-Time Recommendations Fraud Detection Network & IT Operations Master Data Management Graph-Based Search Identity & Access Management Common Graph DB Use Cases
  29. 29. 29 Ebay powers its machine learning based ‘shopbot’ with Neo4j knowledge graph "Feels like talking to a friend" ” Online Shopping
  30. 30. Ebay powers its machine learning based ‘shopbot’ with Neo4j knowledge graph 30 Online Shopping • 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 Solutions and benefits "Feels like talking to a friend" ”
  31. 31. UBS is using Neo4j to manage their complex data infrastructure of over 400 integration points across 18 data- domains to improve access for data consumers. UBS uses Neo4j for trustworthy data Financial Services | Master Data Management • Dramatic improvement to data distribution flow • Knowledge Base improves Ad-hoc analytics • Data governance, lineage and trust improved across entire company • Better service level from IT to data consumers Solutions and benefits
  32. 32. “Neo4j’s high performance engine provides flexibility of data representation along with features that go beyond traditional relational databases.” ” — Sebastian Verheughe, Telenor Telenor uses Neo4j to provide complex 
 self-service 32 Telecom | Identity and Access Management Telenor uses Neo4j to provide businesses and residential customers with a self-service portal that brings together information about corporate structures, subscription information, price plan and owner/payer/user data, billing accounts and any discount agreements. • Shifted authentication from Sybase to Neo4j • Moved resource graph to Neo4j • Replaced batch process with real-time login responses • Mitigated customer retention risks
  33. 33. Marriott is using Neo4j to allow hotel managers to control room rate price optimization across 1.5 million rooms on a daily basis. Marriott reinvents room rate pricing with Neo4j Travel Services | Pricing Recommendations • Created a graph per hotel for 4500 properties in 3 clusters • Enabled a 1000% increase in volume over 4 years while cutting infrastructure cost in half. • "Use Neo4j Support!" “We couldn’t have done this without Neo4j commercial support” ” Scott Grimes Senior Director of Revenue Management, Marriott International  “With Neo4j, we’ve been able to take our average processing time [for pricing operations] from over four minutes to about 13 seconds…and reduce our overall infrastructure cost by about 50%.”  
 – Scott Grimes, Marriott
  34. 34. Product Overview
  35. 35. ı Neo4j: #1 Database for Connected Data Neo4j is an enterprise-grade native graph database that enables you to: • Store and access data and relationships • Traverse data at any levels of depth in real-time • Add and connect new data on the fly Designed, built and tested natively for graphs from the start to ensure: • Performance • ACID Transactions • Agility • Developer Productivity • Hardware Efficiency
  36. 36. Q R Q R Using Other NoSQL to Join Data Using Neo4j Slow queries due to index lookups & network hops Lightning-fast queries due to replicated in- memory architecture and index-free adjacency Relationship Queries on non-native Graph Architectures MACHINE 1 MACHINE 2 MACHINE 3 UNIFIED, IN MEMORY MAP
  37. 37. Real-Time Query Performance Relational and Other NoSQL Databases ResponseTime Connectedness and Size of Data Set 0 to 2 hops 0 to 3 degrees Few connections 5+ hops 3+ degrees Thousands of connections 1000x Advantage “Minutes to milliseconds” Neo4j
  38. 38. How Fast is Fast? *6 machines, each with 48 VCPUs, 256 GB disk and 256 GB of RAM; ~10M node, ~100M relationship graph Workload Non-native graph DB* Neo4j: single thread Count nodes Count outgoing rels Count outgoing rels at depth 2 Count outgoing rels at depth 3 Group nodes by property val Group rels by type Count depth 2 knows-likes Page Rank 201s 202s 276s 511s 212s 198s 324s 2571s < 1ms < 1ms 23s 423s 8s 54s 149s 27s
  39. 39. Keeping Your Graph Intact Essential for Graph Operations Atomic Causal Consistency The graph transaction moves together as one ACID transaction with built-in safety Guarantees Graph Consistency Graph Writes: Neo4j vs. Non-Native Distributed
  40. 40. Non-Native Graph DB Keeping Your Graph Intact Essential for Graph Operations Atomic Causal Consistency Non-Atomic Eventual Consistency The graph transaction moves together as one ACID transaction with built-in safety Without atomic, ACID graph transactions the view of the graph & its property values is necessarily inconsistent Guarantees Graph Consistency Not Good Enough for Graphs Graph Writes: Neo4j vs. Non-Native Distributed
  41. 41. Neo4j Supported Platforms On-Premise Platforms Cloud Platforms and Containers IBM POWER For Development ...and others
  42. 42. 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 Neo4j: Built for the Enterprise
  43. 43. Solution Architecture CyberSecurity Example 43 Nav Mathur Sr. Director Global Solutions @ Neo4j @nav_mathur, in/navmathur
  44. 44. 44
  45. 45. 45 USE ISSUES Terminal ATM- skimming Data Breach Card Holder Card Issuer Fraudster USE $5MAKES $10 MAKES $2 MAKES MAKES $4000 AT Testing Merchants ATMAKES Tx
  46. 46. Money Transferring Purchases Bank Services Relational database Develop Patterns Data Science-team + Good for Discrete Analysis – No Holistic View of Data-Relationships – Slow query speed for connections
  47. 47. Money Transferring Purchases Bank Services Relational database Data Lake + Good for Map Reduce + Good for Analytical Workloads – No holistic view – Non-operational workloads – Weeks-to-months processes Develop Patterns Data Science-team Merchant Data Credit Score Data Other 3rd Party Data
  48. 48. Neo4j powers 360° view of transactions in real-time Neo4j Cluster SENSE Transaction stream RESPOND Alerts & notification LOAD RELEVANT DATA Relational database Data Lake Visualization UI Fine Tune Patterns Develop Patterns Data Science-team Known Threats Known Vulnerabilities Data-set used to explore new insights IBM Identity & Access Mgmt. IBM BigFix Endpoint Security IBM QRadar Log Management Virus Signatures
  49. 49. Business Value Real-Time Fraud Prevention • $MM recaptured in savings • Increased revenue & customer satisfaction due to decreased false positives Graph-Based Analytics • Increased effectiveness of Data Science Team • Months of Analysis now done in minutes & hours • Graph-based visualization accelerates complex analysis of patterns Typical Customers Graph-Based Sense & Respond Architecture for SIEM Financial Services Government Telecom Events & Transactions
  50. 50. Your Engines 
 & Algorithms Customer Facing Apps Native Graph 
 Database Customer 
 Data Sources Complete Solution Real-Time Threat Management Snapshots:
 Modeling & Predictive VisualizationImpact 
 Analysis Recommendations Anomaly Detection Decisioning Variance
 Analysis Rules
 Engine Complex Event
 Procedures APIs & Drivers Data Ingest CMDB Assets Data Network Monitoring Data Application Monitoring Data Bus. Process Monitoring Data Data Lake Threat Vectors Mitigation Patterns
  51. 51. 51 ON Neo4j On IBM Power8 Real - Time Graph Processing That’s Entirely In-Memory Richard Sheppard Director of Sales @ Blair Technology Solutions
  52. 52. 4X Threads per core* 4X Mem. Bandwidth* 4X More cache* @ Lower Latency These design decisions result in best performance for data centric workloads like: Database, NoSQL, Big Data Analytics, OLTP POWER8 SMT8 x86 Hyperthread Parallel Processing POWER8 pipe Data flow x86 pipe POWER8 x86 POWER8 + OpenPOWER x86 ON
  53. 53. The POWER of an open ecosystem ON 300Worldwide members of 2,500+ Linux ISVs developing on POWER 30 Hardware and technology providers 100,000+ Open source packages100+ Collaborative innovations under way
  54. 54. innovations under way POWER8 with CAPI enabled acceleration running Neo4j delivers 
 1.61X the price-performance versus Intel Xeon E5-2650 v4 with NVMe IBM Power S822LC (20-core, 128GB) HP DL380 Gen9 (24-core, 128GB) Server price* -3-year warranty $19,123 $16,911 Mixed graph transaction Workload (total operations per second) 711 390 1.61X
 Price-Performance 1.82X
 Performance per Server • Based on IBM internal testing of single system and OS image running mixed graph transaction s based on 200 GB data model internal IBM and Neo4j workload. Conducted under laboratory condition, individual result can vary based on workload size, use of storage subsystems & other conditions. Data as of October 19, 2016 • IBM Power System S822LC; 20 cores (2 x 10c chips) / 160 threads, POWER8; 128 GB memory (16 x 8GB), 1.6 TB CAPI NVMe adapter , Neo4j 3.0.4, Ubuntu 16.04. Competitive stack: HP Proliant DL380 Gen9; 24 cores (2 x 12c chips) / 48 threads; Intel E5-2650 v4; 128 GB memory,(16 x 8GB), 1.6 TB NVMe adapter, Neo4j 3.0.4, Ubuntu 15.10. * Pricing is based bundled pricing for S822LC with Integrated CAPI Flash card (IBM ordering system) and HP Web price https://h22174.www2.hp.com/SimplifiedConfig/Index ON
  55. 55. Scalability Only POWER8 can provide up to 56 terabytes of extended memory space with up to 40 terabyte CAPI flash architecture. Performance TCO Services Why Neo4j on Linux On POWER Reduced downtime, HA database monitoring / management and data integration for mission critical enterprise applications. Offer ability to use Flash as extended memory without compromising real-time capabilities . Master datasets connecting data inside a single graph inventory or supply chain management data for global enterprise manufactures.
  56. 56. 56 ➢ Trusted IBM Business Partner for 21 years ➢ Enabling business to utilize data for improved business insights ➢ Certified IBM POWER server (AIX, i, Linux) leader ➢ Contact us to discuss your challenges in adopting graph database to meet business needs Richard Sheppard rsheppard@blairtechnology.com 905-474-4206
  57. 57. Next Steps Register for a brown-bag graph talk with your team @ https://neo4j.com/brownbag/ Attend GraphConnect use 50% off code IBMCD50 Thanks!57 Nav Mathur Sr. Director Global Solutions @ Neo4j @nav_mathur, in/navmathur Richard Sheppard Director of Sales @ Blair Technology Solutions Amy Hodler Sr. Marketing Mgr @ Neo4j @amyhodler, in/amyhodler

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