This document discusses whether a graph database should be part of an organization's next data warehouse stack. It introduces Anzo, a managed data fabric that connects and blends enterprise data, and AnzoGraph, a data mart that can be embedded in applications or used with ETL and visualization tools. Graph databases are growing in popularity due to their ability to ask complex questions across complex data in a way that is not possible with SQL. Graph databases also support powerful graph algorithms and analysis not found in SQL. AnzoGraph is a graph database that provides standard analytics capabilities along with graph-specific features.

1. Should a Graph Database be
in your Next Data Warehouse
Stack?
Presenters:
Barry Zane, VP Engineering Steve Sarsfield, VP Product

2. ©2018 Cambridge Semantics Inc. All rights reserved.
Relationship Between Anzo & AnzoGraph
Our topic today:
Anzo - Managed Data Fabric
Connect and blend enterprise data
Ingestion & Mapping
Rich data management, harmonization,
provenance
Uses AnzoGraph as an engine AnzoGraph - Data Mart in your
warehouse stack
Embed a graph database in your
application or write queries directly
Use with your own, or third-party ETL
and visualization tools
1001 other uses

3. Graph’s time
has come.
- Gartner - Top 10 Data and Analytics Technology Trends for 2019
“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”

4. ©2018 Cambridge Semantics Inc. All rights reserved.
What is Driving People to Graph
Additional powerful
graph analysis, not
present in SQL Graph Algorithms and capabilities
Page Rank
Shortest Path
All Path
Label Propagation
Weakly Connected Components
K neighborhood
Counting Triangles
Inferences (RDFS+)
Advanced Grouping Sets
Labeled Property Graphs (RDF*)
Why you should care:
Use Cases
• R&D Acceleration (pharma)
• Fraud Detection
• Recommendation Engines
• Network and IT operations
• Search
• Master data management
• Machine Learning

5. ©2018 Cambridge Semantics Inc. All rights reserved.
…but with Standard Analytics Present
All of the standard
analytics are still
available
• BI-style analytics
• Aggregates
• Sub-queries
• Views
• User defined extensions
FROM
Multiple Graphs or Default Graph
WHERE
Joins/Traversals
GROUP BY
Sum/Count/Min/Max/Avg/Std/Var
GroupConcat/Sample
ORDER BY
Offset & Limit
HAVING
Built-in functions
80+ functions on strings, numbers, dates, times
Why you should care:
• You aren’t losing any analytical
capabilities

6. ©2018 Cambridge Semantics Inc. All rights reserved.
Analytic Systems Complement Transactional systems
Analytics (OLAP)
Aggregations and multi-way joining
across broad swaths of the data
Inserts are primarily bulk loads
Query: “Tell me about population
trends”
Transactions (OLTP)
Fetches and relations between a small
set of subjects
Inserts are primarily single-subject
Query: “Tell me about Barry”
Redshift
Handles inserts
and fetches,
just slowly
MySQL
Can do analytical
workloads,
just slowly
The bigger the data, the more this
difference is noticed.

7. ©2018 Cambridge Semantics Inc. All rights reserved.
Table Stakes for Modern Analytical Systems
Massively Parallel
Processing (MPP) -
Scalable BI Analytics
• Scalable to hundreds
of compute servers -
trillions of
nodes/edges
Standards Driven
• Relational ANSI SQL
• AnzoGraph W3C
SPARQL,
OpenCypher
Automatic Analytics
Optimization
• Compiled Queries,
Automatic, invisible
code generator
• First query of a
given shape is
slower
• Subsequent query
runs of that shape
at “physics speed”
Simple Deployment
• No customer-defined
indexes
• Load and Go
• Cloud, containers,
on-premises
Analytics Speed, Simplicity, Lower Costs

8. What is Graph?
A Wonderful Way To Think About Data

9. ©2018 Cambridge Semantics Inc. All rights reserved.
Graph Nodes and Edges - Simple, but Rich
• More like Human processing of the world.
• Graphs do not require pre-created schema.
– New Properties immediately usable
• Possible in SQL schema, but need to make
schema choices:
• Possibly:
– Person Table [name, birthday…]
– Places Table [height…]
– friend Join Table [person1, person2]
– wentUp Join Table [person, place]
– has Join Table [place, property]
type: <Boy>
birthday: 09/17/1975
Jack type: <Girl>
Jill
TheHill
type: <Place>
height: 1500 feet
has: Grass
has: Trees
partOf: <TheMountain>

10. ©2018 Cambridge Semantics Inc. All rights reserved.
type: <Boy>
birthday: 09/17/1975
Jack type: <Girl>
Jill
TheHill
type: <Place>
height: 1500 feet
has: Grass
has: Trees
partOf: <TheMountain>
Subject Predicate Object (Hint)
Jack type <Boy> label
Jack birthday 09/17/1975 property
Jack friend <Jill> edge
Jack wentUp <TheHill> edge
Jill type <Girl> label
Jill wentUp <TheHill> edge
TheHill type <Place> edge
TheHill has Water property
TheHill has Trees edge
TheHill partOf <TheMountain> edge
TheHill height 1500 property
EVERYTHING is expressed as atomic “Triple Statements” (RDF)

11. ©2018 Cambridge Semantics Inc. All rights reserved.
Let’s Add Triples for Inferences - Semantic Enrichment
type: <Boy>
birthday: 09/17/1975
type:<Man>
type:<Person>
gender:Male
Jack
type: <Girl>
type: <Woman>
type: <Person>
gender: Female
Jill
TheHill
type: <Place>
height: 1500 feet
has: Grass
has: Trees
partOf: <TheMountain>
Subject Predicate Object (Hint)
Jack type <Boy> label
Jack birthday 09/17/1975 property
Jack friend <Jill> edge
Jack wentUp <TheHill> edge
Jill type <Girl> label
Jill wentUp <TheHill> edge
TheHill type <Place> edge
TheHill has Water property
TheHill has Trees edge
TheHill partOf <TheMountain> edge
TheHill height 1500 property
Boy subClass <Man>
Man gender Male
Man subClass <Person>
Girl subClass <Woman>
Woman gender Female
Woman subClass <Person>
friend property Reflexive

12. ©2018 Cambridge Semantics Inc. All rights reserved.
Let’s add Edge Properties (LPG RDF*)
type: <Boy>
birthday: 09/17/1975
Jack type: <Girl>
Jill
TheHill
type: <Place>
height: 1500 feet
has: Grass
has: Trees
partOf: <TheMountain>
Edge properties have been the primary
historical difference between RDF and
LPG graph engines.
The RDF* is a slight extension of RDF
to reference/store these properties.
While not yet a full W3C standard,
RDF* is supported by multiple vendors
and is on the standards-track.
AnzoGraph fully supports the
RDF*/LPG model.
Essentially, edge properties are triples
about other triples.

13. ©2018 Cambridge Semantics Inc. All rights reserved.
How We Load Relational Tables to AnzoGraph
• Rows —> Nodes
– Value fields —> Node Properties
• Example: Birthdate, Gender, SSN…
– Foreign keys —> Edges
• Example: Father, Mother…
• Join-tables Disappear!
– Multi-value Properties & Edges
• Example: Friends, Credit Cards…
• NULLs disappear
– Example: If birthday unknown, that node-property not present

14. ©2018 Cambridge Semantics Inc. All rights reserved.
Similarities, Differences -> Power
• Like SQL Relational but…
• No explicit schema. The Ontology (fancy word for schema) in the data.
• Further ontology information may also be called out in the data, such as inference rules.
• Standard SQL aggregates, joins, etc, but simple and powerful relationship capabilities.
• “How is Jack related to Jill?” (upcoming slide)
– In SQL Relational
• Are they spouses?
• Are they siblings?
• Are they friends?
• Do they have the same hobby?
• … enumerate the choices, EXPLODES with degrees of separation
– In SPARQL/Cypher Graph
• How is Jack related to Jill?
• … you can directly specify degrees of separation
• Pretty exciting, essentially all the power of SQL, but you can do more, with more diverse
data, where the data tells you about itself, rather than you knowing in advance.

15. Graph Queries

16. ©2018 Cambridge Semantics Inc. All rights reserved.
You know SQL, introducing LPG SPARQL*, Cypher!
• SPARQL* - New Labelled Property Graph (LPG)
– SQL-like - Standard Keywords (SELECT, WHERE…)
– W3C Standard (LPG aspect de-facto)
– Most attractive to SQL folks
– Amazon Neptune, Jena, AllegroGraph …
• Cypher
– New Keywords (MATCH vs. WHERE)
– De-facto market standard
– Most attractive to programmers
– Neo4j, Redis, …
Not a repeat of Beta vs. VHS
Both can work on the Same data
on the Same system.
They do the same things - is
really a matter of taste!
AnzoGraph has SPARQL*,
soon Cypher… GQL when
available

17. ©2018 Cambridge Semantics Inc. All rights reserved.
Discovery Inference
SELECT $thing $type
WHERE {
$thing <type> $type
}
SELECT $edge
WHERE {
<Jack> $edge <Jill>
}
How is Jack related to Jill? What kind of things are in the graph?
thing | type
+------+------
Jack | Boy
Jill | Girl
Jack | Man
Jill | Woman
Jack | Person
Jill | Person
TheHill| Place
edge
------------
friend
Returns:
Returns:
Queries in SPARQL… Can just as easily be in Cypher
The “scan” in the WHERE clause is ALWAYS
Subject, Predicate, Object as in the RDF!

18. ©2018 Cambridge Semantics Inc. All rights reserved.
Discovery Analytics
SELECT $jack $common $jill
WHERE {
<Jack> $jack $common .
<Jill> $jill $common .
}
GROUP BY $type
What do Jack and Jill have in common?
jack | common | jill
----------+---------+--------
wentUp | TheHill | wentUp
worksWith | Harry | brother
friend | Aesop | friend
Both Jack and Jill wentUp TheHill.
Jack worksWith Jill’s brother Harry.
They both have a friend Aesop.
Queries in SPARQL… Can just as easily be in Cypher
SELECT $state $type (COUNT(*) AS $cnt)
WHERE {
$person <type> $type .
$person <livesIn> $zipcode .
$zipcode <in> $state .
FILTER($type=<Man> || $type=<Woman>)
}
GROUP BY $state $type ORDER BY $state
Number of Men & Women By State
state | type | cnt
-----------+-------+-----------
Alabama | Man | 2,355,456
Alabama | Woman | 2,456,642
Alaska | Man |351,455
Alaska | Woman | 350,637
California | Man | 19,344,567
California | Woman | 20,454,679
...
Triples clauses with same variable are “joined”

19. Simple, Powerful, Next Generation
Download or Deploy to AWS Cloud:
www.anzograph.com

20. Begin your journey to graph today!
Watch the On-demand Webinar
Or to learn more, go to www.anzograph.com