5. Frederik Obermaier, Süddeutsche Zeitung, on the
importance of networks in journalism. From Panel at
Columbia University Feb 23, 2018.
“I’ve only come
across 3 or 4
stories in my
career that
weren’t about
networks.”
8. 2.6 TB
11.5 million documents
Emails, Scanned Documents,
Bank Statements etc… Person
B
Bank US
Account
123
Person
A
Acme
Inc
Bank
Bahama
s
Address
XNODE
RELATIONSHIP
9. 2.6 TB
11.5 million documents
Emails, Scanned Documents,
Bank Statements etc…
14. Common Graph Use Cases
Fraud
Detection
Real-Time
Recommendations
Network & IT
Operations
Master Data
Management
Knowledge
Graph
Identity & Access
Management
airbnb
15. “Forrester estimates that over 25% of
enterprises will be using graph
databases by 2017.”
Forrester, 2014
16. DB-engines Ranking of Database Categories
• Graph DBMS
• Key-value stores
• Document stores
• Wide column store
• RDF stores
• Time stores
• Native XML DBMS
• Object oriented DBMS
• Multivalue DBMS
• Relational DBMS
Graph DB
2013 2014 2015 2016 2017 2018 2019
Popularity of Graphs
17. Trend No. 5: Graph
…
The application of graph processing and graph DBMSs will grow at 100
percent annually through 2022 to continuously accelerate data preparation
and enable more complex and adaptive data science.
…
Graph analytics will grow in the next few years due to the need to ask
complex questions across complex data, which is not always practical
or even possible at scale using SQL queries.
https://www.gartner.com/en/newsroom/press-releases/2019-02-18-gartner-identifies-top-10-data-and-analytics-technolo
February 18, 2019
20. Strictly Confidential
Neo4j Aura – Announced 11/6
20
Flexible Reliable
● Zero Administration
● On-Demand Scaling
● Simple, consumption
based pricing
● Always-On and self-healing,
clustered configuration
● Data Integrity & Durability
● Secure including end-to-end
encryption
● Native graph performance
● World’s most popular graph query
language
● Broad language support - drivers
for Java, .NET, JavaScript,
Python, Go, Spring, etc.
Developer Friendly
The world's most flexible, reliable and developer-friendly
graph database as a service.
21.
22. Retail
7 of top 10
Finance
20 of top 25 7 of top 10
Software
Hospitality
3 of top 5
Telco
4 of top 5
Airlines
3 of top 5
Logistics
3 of top 5
76%
FORTUNE 100
have adopted or
are piloting Neo4j
23.
24. • Free access for startups with up to 50 employees;
under $3M in revenue
• Neo4j Enterprise Edition
• Neo4j Bloom
• Apply at http://neo4j.com/startup-program
• Notable alumni include:
Medium
Neo4j Startup Program Expansion
25. Background
• Social network of 10M graphic artists
• Peer-to-peer evaluation of art and works-in-progress
• Job sourcing site for creatives
• Massive, millions of updates (reads & writes) to Activity
Feed
• 150 Mongos to 48 Cassandras to 3 Neo4j’s!
Business Problem
• Artists subscribe, appreciate and curate “galleries” of
works of their own and from other artists
• Activities Feed is how everyone receives updates
• 1st implementation was 150 MongoDB instances
• 2nd implementation shrunk to 48 Cassandras, but it
was still too slow and required heavy IT overhead
Solution and Benefits
• 3rd implementation shrunk to 3 Neo4j instances
• Saved over $500k in annual AWS fees
• Reduced data footprint from 50TB to 40GB
• Significantly easier to introduce new features like,
“New projects in you Network”
Adobe Behance Social Network of 10M Graphic Artists
Social Network25
EE Customer since 2016 Q
28. Background
• Largest Cable TV & Internet Provider in US
• 3rd Largest network on the planet
• xFi is consumer experience in 3M houses
• Internet, router, devices, security, voice & telephony
• Transformational customer experience
Business Problem
• Integrate all experience in a smart home
• Create innovative ideas based on cross-platform and
household member preferences
• Add integrated value of xFinity triple play & quad-
play services (internet, VoIP, cable TV & home
security)
Solution and Benefits
• Custom content per household member
• Security reminders (kids are home, garage left open)
• Serves millions of households
• Makes content recommendations based on occupant,
time of day, permissions and preferences
• Has Siri-like voice commands
COMCAST Xfinity xFi TELECOMMUNICATIONS
Smart Home / Internet of Things28
EE Customer since 2016 Q
31. Strictly ConfidentialStrictly Confidential
The Market Sees Strong Synergy between Graphs and
Artificial Intelligence
31
AI research papers focused on graphs
SURGING
INTEREST
New Book:
20K Downloads in first 2 weeks
CONNECTED
CONTEXT FOR AI/ML
CUSTOMER
TRACTION
German Center for
Diabetes Research
Get list of companies attending…
“We have an exciting day ahead of us. Let me take this first hour to take a step back and talk a little about the state of the graph space today, and much more importantly talk about where I believe the space is going.
“It’s been a year since we had a GraphConnect, and what a year it has been. Graphs have had an impact on an order that we’ve never seen before. Let me give you a couple of examples.”
GA – longstanding partner expanded from EMEA. Help with full lifecycle from evaluating a use case through deployment.
NEORIS – One of our newest partners, but long in knowledge graph experience and the business of Latin America
VinkOS – a partner making terrific investments in Neo4j and bringing the value of graphs to its large number of Pentaho customers
AgaveLink – special thanks to our oldest partner in Mexico for evangelizing graphs in Mexico and beyond
Add agenda for the day…
Who has heard of graphs/Graph db?
Who has heard of knowledge graphs?
“The first story is about the Panama Papers, which was the biggest news story of 2016, but its impact is still very live: a couple of months ago the prime minister of Pakistan resigned over findings in the Panama Papers, and just last week he was actually formally indicted for corruption.”
“In this particular story, the heroes are two journalists at the Suddeutsche Zeitung who were provided with a”<click>
“2.6 TB of leaked, that supposedly contained data detailing accounts and activities of the powerful and the wealthy for legal tax planning, but possibly also for illegal tax evasion.”
“So they got this 2.6 TB huge data dump of spaghetti information and they wanted to make sense of that. They ran it through an open source pipeline of technologies and ended up with”<click>
”11 MILLION documents, which btw is the largest leak in journalistic history. In these documents are emails, bank accounts, names, addresses etc, and they have to make sense of all that and uncover any newsworthy stories.”
“Now let’s take a step back from data and technology and just think about what investigative journalism is. IJ is all about finding patterns. Here’s an example of a pattern:”
<click> Person has Account with Bank. Yadayada, nothing wrong. Blabla lives on address.
“Now if we look at this more abstract we can see that we have concepts and how they are related to each other.”
“In the graph world we call these<CLICK>Nodes and<CLICK>Relationships.”
“It turns out with these very simple abstractions — <enumerate them> — we can build and model *everything*. It turns out that this model is very flexible. Easy to evolve. Etc.”
… “and your data model will organically evolve with you as as your needs change.”
“What’s equally amazing is if you wrap this data model in an infrastructure that can support not just 7 nodes but”<click>
“a million nodes, or 11.5 million nodes, or a billion nodes, or 100 billion nodes.”
“Ok, so back to our story. Remember that second pattern we discussed before, where someone was connected through his wife to an offshore bank account. Well, here’s the real world example of that: the Icelandic prime minister Sigmundur Gunnlaugsson. Excuse me! The *former* prime minister of Iceland. That’s the type of impact the Panama Papers had.”
“As mentioned, it rapidly became one of the biggest news stories last year and was written up in virtually every major newspaper in every country in the world.”
And of course when they do something like this something-something last month
At the time, this was considered a bold and shocking prediction.
Since 2013, Graph database have exploded in popularity. As much as we’re proud of what we’ve done with the product and the company, this is a function of the AUDIENCE - the global fan base for Neo4j that keeps finding more and more ways to use it.
Gartner tends to advise large, conservative enterprises and they validate technologies that are ready for that audience. We were pleased to see them put graph databases in their Top 10 Data and Analytics trends for 2019.
Another sign of markets going mainstream is the availability of skills. This is a big part of why you see major Systems Integrators like EY, Capgemini, Accenture, Deloitte and others developing graph practices and attending GraphTour.
UpWork is a very popular freelancer site where people hire individual workers for projects ranging from graphic design through software development. Their Q2 2019 index showed Neo4j as the 11th fastest-growing skillset in terms of market demand. And we’re in good company with Salesforce Lightning, Moz which is a ubiquitous tool for webmasters, and Azure.
<note: build slide>
And the database market passed an historic milestone in October 2019. Neo4j originally developed a very elegant, declarative, graph-optimized query language called Cypher. In the interest of users and the graph category, we open sourced it as openCypher and it got adoption from a wide range of vendors including SAP, Redis, and multiple startups. Not surprisingly, Oracle was working in parallel to add SQL extensions to accommodate property graphs. We’ve been on a years-long journey to work with the vendor and developer community to get to a single integrated standard that builds many Cypher concepts while adding the best of other graph languages.
In October 2019, ISO the International Standards Organization blessed the first new database query language in more than 35 years when it blessed the original SQL specification.
This is incredibly good news for graph database buyers, users, and practitioners. Standards reduce vendor lock-in, make skills more available and transferrable, and simplify integration and interoperability.
Neo4j Aura is the simplest way to run Neo4j in the cloud. Completely automated and fully-managed, Neo4j Aura delivers the world’s most flexible, reliable and developer-friendly graph database as a service. With Neo4j Aura, you leave the day-to-day management of your database to the same engineers who built Neo4j, freeing you to focus on building rich graph-powered applications.
What you see is a real technology marketplace firming up. Our partner GraphAware put this together. We’ve talked a lot about graph databases but like all databases, you need to be able to get data in, get it out and expose it to different kinds of users, enrich it with analytics, educate your developers and admins, and more. You can see there is a wide range of tools, and training resources available as you advance your graph journey.
We see this at Neo4j, where as of today 76% of the F100 have either piloted or adopted Neo4j! That’s a staggering amount.
But that’s not enough. As of right now, most of the leading organizations in most of the biggest verticals in the world rely on Neo4j. We already talked about Software and Insurance, but just to give you a sense: 20 of the top 25 global financial services organizations (and 20 of the top 20 US banks) are using Neo4j, 4 of the top 5 telcos and 3 of the top 5 airlines. Graphs have truly arrived in the enterprise.
“And today, we have 470 startups in that program. Look at these logos. You may not recognize all of them, or maybe even one of them. But everyone of them has the power of Google in their hand. And I’ll be willing to bet that at least one out of these 470 startups will become a household name in the next ten years.”
I’d like to close with a topic that you’ve all heard about, and that many of you may already be working in, and that’s AI. And more precisely, how graphs are starting to be used in AI.
Those of you who were at GraphConnect in NYC last year may remember this picture.
It’s a taxonomy of different kinds of machine learning. What’s really obvious looking at the images it’s very clear that graphs are foundational for Machine Learning!
This then begs the question: how can I use graphs to help with own my AI problem.
Why do other databases also talk about the same use cases?
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