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Knowledge  Graphs
Journey	
  of	
  the	
  Connected	
  Enterprise	
  
Benjamin	
  Nussbaum	
  
@bennussbaum	
  |	
  ben@atomrain.com	
  
www.atomrain.com	
  |	
  www.graphgrid.com	
  
In  the  Past…  Leonhard  Euler  1707-­‐1783
These  are  graphs…
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.	
  
IntuiDve  
Speed  
Agility
Data  used  to  be  stored  like  this…
Then  we  started  storing  it  like  this…
Now  we  can  store  data  like  this…
But  why  does  this  make  sense?
Because  your  data  really  IS  connected  like  this
Graph  Thinking:  IdenDty  &  Access  Management
Graph  Thinking:  Graph  Based  Search
Graph  Thinking:  Master  Data  Management
Graph  Thinking:  Fraud  DetecDon
Graph  Thinking:  Network  &  IT  OperaDons
Graph  Thinking:  Cyber  Threat  DetecDon
Graph  Thinking:  Unlocking  Understanding
Your	
  Data	
  Connected	
  is	
  Your	
  Knowledge	
  Graph	
  
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	
  
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
Improved  Data  Understanding  and  InteracDon
Improved  Cross-­‐FuncDonal  CollaboraDon
Graphs	
  Connect	
  Not	
  Only	
  Your	
  Data	
  But	
  Your	
  Whole	
  Organiza=on	
  
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	
  
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.	
  
Key  Components  of  a  NaDve  Graph  Database
Key  Components  of  a  NaDve  Graph  Database
Key  Components  of  a  NaDve  Graph  Database
How  do  we  go  from  Big  Data  to  Smart  Data?
Graph  Thinking  is  a  Paradigm  ShiY
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	
  
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.	
  
Using  a  non-­‐naDve  graph  is  like  keeping  your  
old  dirt  bike  engine  for  your  new  race  car
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	
  
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	
  
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)	
  
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
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.	
  
Thank  You!
Knowledge	
  Graphs:	
  Journey	
  of	
  the	
  Connected	
  Enterprise	
  
Benjamin	
  Nussbaum	
  
@bennussbaum	
  |	
  ben@atomrain.com	
  
www.atomrain.com	
  |	
  www.graphgrid.com	
  

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Knowledge Graphs - Journey to the Connected Enterprise - Data Strategy and Analytics

  • 1. Knowledge  Graphs Journey  of  the  Connected  Enterprise   Benjamin  Nussbaum   @bennussbaum  |  ben@atomrain.com   www.atomrain.com  |  www.graphgrid.com  
  • 2.
  • 3. In  the  Past…  Leonhard  Euler  1707-­‐1783
  • 5. 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.  
  • 7. Data  used  to  be  stored  like  this…
  • 8. Then  we  started  storing  it  like  this…
  • 9. Now  we  can  store  data  like  this…
  • 10. But  why  does  this  make  sense?
  • 11. Because  your  data  really  IS  connected  like  this
  • 12. Graph  Thinking:  IdenDty  &  Access  Management
  • 13. Graph  Thinking:  Graph  Based  Search
  • 14. Graph  Thinking:  Master  Data  Management
  • 16. Graph  Thinking:  Network  &  IT  OperaDons
  • 17. Graph  Thinking:  Cyber  Threat  DetecDon
  • 18. Graph  Thinking:  Unlocking  Understanding Your  Data  Connected  is  Your  Knowledge  Graph  
  • 19. 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  
  • 20. 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
  • 21. Improved  Data  Understanding  and  InteracDon
  • 22. Improved  Cross-­‐FuncDonal  CollaboraDon Graphs  Connect  Not  Only  Your  Data  But  Your  Whole  Organiza=on  
  • 23. 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  
  • 24. 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.  
  • 25. Key  Components  of  a  NaDve  Graph  Database
  • 26. Key  Components  of  a  NaDve  Graph  Database
  • 27. Key  Components  of  a  NaDve  Graph  Database
  • 28. How  do  we  go  from  Big  Data  to  Smart  Data?
  • 29. Graph  Thinking  is  a  Paradigm  ShiY
  • 30. 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  
  • 31. 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.  
  • 32. Using  a  non-­‐naDve  graph  is  like  keeping  your   old  dirt  bike  engine  for  your  new  race  car
  • 33. 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  
  • 34. 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  
  • 35. 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)  
  • 36. 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
  • 37. 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.  
  • 38. Thank  You! Knowledge  Graphs:  Journey  of  the  Connected  Enterprise   Benjamin  Nussbaum   @bennussbaum  |  ben@atomrain.com   www.atomrain.com  |  www.graphgrid.com