1.
Knowledge Graphs
Journey
of
the
Connected
Enterprise
Benjamin
Nussbaum
@bennussbaum
|
ben@atomrain.com
www.atomrain.com
|
www.graphgrid.com

2.
In the Past… Leonhard Euler 1707-­‐1783

3.
These are graphs…

4.
More Recently…
• The
understanding
and
discovery
that
comes
through
connec@ons
is
something
that
organiza@ons
like
Google
(Knowledge
Graph)
and
Facebook
(Social
Graph)
have
leveraged
very
well
for
over
a
decade.
• We
owe
the
rising
popularity
and
availability
of
the
general
purpose
graph
databases
today
to
the
pioneers
of
the
space,
Neo
Technology,
the
makers
of
Neo4j
the
one
truly
produc@on
ready
na@ve
graph
database
with
over
15
years
of
history.
• Now
every
organiza@on
can
have
a
knowledge
graph.

5.
IntuiDve
Speed
Agility

6.
Data used to be stored like this…

7.
Then we started storing it like this…

8.
Now we can store data like this…

9.
But why does this make sense?

10.
Because your data really IS connected like this

11.
Graph Thinking: IdenDty & Access Management

12.
Graph Thinking: Graph Based Search

13.
Graph Thinking: Master Data Management

14.
Graph Thinking: Fraud DetecDon

15.
Graph Thinking: Network & IT OperaDons

16.
Graph Thinking: Cyber Threat DetecDon

17.
Graph Thinking: Unlocking Understanding
Your
Data
Connected
is
Your
Knowledge
Graph

18.
What are the benefits of a knowledge graph?
• You’re
interac@ng
with
your
data
in
its
true
form
• Everyone
can
understand
the
data
design
and
organiza@on
• Developers
get
more
done
in
less
@me
• Your
organiza@on’s
data
is
connected
across
all
silos
• Understanding
the
connec@ons
is
now
possible

19.
Improved Data Understanding and InteracDon
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN
JOIN

20.
Improved Data Understanding and InteracDon

21.
Improved Cross-­‐FuncDonal CollaboraDon
Graphs
Connect
Not
Only
Your
Data
But
Your
Whole
Organiza=on

22.
Improved Developer ProducDvity
“Complex
Join”
in
SQL
opencypher.org
–
Na@ve
Query
Language
for
Graphs
SQL
Query
vs
Na@ve
Graph
Query
(Cypher)
Equivalent
queries
for
finding
the
repor@ng
chain
within
an
organiza@on

23.
Key Components of a NaDve Graph Database
• Nodes
–
The
“things”
in
your
data.
• Rela@onships
–
The
context
of
how
two
“things”
are
related.
They
are
treated
as
first
class
en@@es.
• Labels
–
Think
of
these
as
tags
used
to
organize
Nodes.
A
Node
can
have
mul@ple
labels
applied
to
it.
• Proper@es
–
Both
Nodes
and
Rela=onships
have
Proper@es.
These
store
a^ributes
of
the
Node/Rela@onship.

24.
Key Components of a NaDve Graph Database

25.
Key Components of a NaDve Graph Database

26.
Key Components of a NaDve Graph Database

27.
How do we go from Big Data to Smart Data?

28.
Graph Thinking is a Paradigm ShiY

29.
And no paradigm has ever made more sense
• This
is
how
the
brain
works
–
dealing
with
“things”
and
how
they’re
related
is
already
how
we’re
wired
to
func@on
• Solve
more
complex
problems
with
less
effort
• Improved
collabora@on
between
technical
teams
and
everyone
else
• Flexibility
to
evolve
your
data
naturally
as
your
business
changes

30.
But a New Paradigm Requires a New Engine
Na=ve
Graph
Database
• Op@mized
for
graph
traversal
• Rela@onships
are
first
class
• Referen@al
integrity
guaranteed
• ACID
Compliant
&
Transac@onal
Non-­‐Na=ve
Graph
Database
• SQL,
Document,
Tabular,
Key
Value,
etc
database
engine
with
an
abstrac@on
layer
that
provides
“graphy”
interac@ons.
• Not
sympathe@c
to
the
nature
of
reading
and
wri@ng
connected
data.

31.
Using a non-­‐naDve graph is like keeping your
old dirt bike engine for your new race car

32.
Choose your Engine carefully
Na=ve
Graph
Database
• Neo4j
Non-­‐Na=ve
&
Not
Graph
Databases
• DataStax
• Elas@c
• IBM
• FlockDB
–
edge
cache
• Tinkerpop
–
compute
framework
• RDF
–
specifica@on

33.
The leading naDve graph database engine
• Neo4j
is
the
world’s
leading
na@ve
graph
database
• Neo4j
is
op@mized
for
connected
data
opera@ons
• Neo4j
is
my
go
to
for
connected
data
solu@ons
in
the
enterprise
• Neo4j
is
used
by
the
world’s
leading
enterprises
• Neo4j
will
accelerate
your
knowledge
graph
ini@a@ves

34.
IntegraDng with Your ExisDng Architecture
• Very
low-­‐risk,
non-­‐invasive
opera@on
• Create
connectors
for
exis@ng
data
bases
• Flow
data
into
your
knowledge
graph
• Real-­‐@me,
analy@cs,
learning,
understanding,
etc
applica@ons
interact
with
the
na@ve
graph
database
directly
• Start
flowing
new
data
directly
into
your
knowledge
graph
(assuming
you
chose
one
that
is
ACID
and
guarantees
referen@al
integrity)

35.
Non-­‐Invasive Architecture OpDon
Data Storage and
Business Rules Execu5on
Data Mining
and Aggrega5on
Applica'on
Graph Database Cluster
Neo4j Neo4j Neo4j
Ad Hoc
Analysis
Bulk Analy'c
Infrastructure
Hadoop, EDW …
Data
Scien'st
End User
Databases
Rela5onal
NoSQL
Hadoop

36.
Where do I go from here?
• Embrace
the
paradigm
shih
–
go
na@ve.
• Embrace
the
paradigm
shih
–
get
connected.
• Embrace
the
paradigm
shih
–
work
intui@vely.
• Embrace
the
paradigm
shih
–
transform
your
organiza@on.

37.
Thank You!
Knowledge
Graphs:
Journey
of
the
Connected
Enterprise
Benjamin
Nussbaum
@bennussbaum
|
ben@atomrain.com
www.atomrain.com
|
www.graphgrid.com