Sean Martin, CTO of Cambridge Semantics, Philip Howard, Research Director at Bloor Research and co-author of “Graph Database Market Update 2020”, and Steve Sarsfield, VP of Product at Cambridge Semantics, hold a fireside chat on the State of the Graph Database Market.

1. Fireside Chat with Bloor Research:
State of the Graph Database Market 2020
Philip Howard, Research Director, Bloor Research
Sean Martin, CTO, Cambridge Semantics
Steve Sarsfield, VP of Product, Cambridge Semantics

2. Agenda
• Trends in the Graph Database Space – Philip Howard
• How Cambridge Semantics Views the Graph DB market – Sean Martin
• Chatting
• Your Questions

3. Housekeeping
Please join the conversation by using the Q&A tab
The webinar will be available for replay (24 hours)
We will be able to get a copy of the slides
We will follow-up on unanswered questions
You will receive a copy of the report next week
YES

4. Trends in the Graph Database space
Philip Howard
Research Director – Bloor Research
JULY 2020

5. Confidential © Bloor Research 2020 Evolution is Essential
Agenda
The latest trends in the graph database market
• Market growth • Scalability and Performance
• Cloud • Operations/transactions and analytics
• Geospatial • Algorithms and notebooks
• RDF* • Knowledge graphs
• Data virtualisation • Ease of use
– Languages
– Migration tools

6. Confidential © Bloor Research 2020 Evolution is Essential
Market growth
Markets and Markets report, August 2019,
estimates the graph database market to grow
to $2.8B by 2024
Gartner – also 2019 –
predicts 100% CAGR in graph database market by 2022

7. Confidential © Bloor Research 2020 Evolution is Essential
Scalability and performance
Many vendors
have historically
focused on
scale-up and not
scale-out

8. Confidential © Bloor Research 2020 Evolution is Essential
Cloud
Public clouds
Managed services
Private clouds

9. Confidential © Bloor Research 2020 Evolution is Essential
Operations/transactions and Analytics
All graph databases support queries
to some extent
Some focus specifically on analytics
All can process operations
Some are ACID compliant
Often HTAP/HOAP(3)

10. Confidential © Bloor Research 2020 Evolution is Essential
Algorithms and notebooks
Various relational database vendors offer
parallelised graph algorithms
But many cannot be usefully parallelised because
of iterative nature of queries (self-joins)
Increasing provision/support by graph vendors
Increasing support for Jupyter
and Zeppelin notebooks

11. Confidential © Bloor Research 2020 Evolution is Essential
Geospatial
Screenshot from Cambridge Intelligence
You don’t just want to know what
happened and you need to know
where it happened
Necessary for many insurance, law
enforcement, logistics, IoT and
other applications

12. Confidential © Bloor Research 2020 Evolution is Essential
RDF* and SPARQL*
Supports the use of labels as in labelled property graphs
– Replaces methods such as reification and other methods that are
either complex, or lead to node proliferation or both
Enables the use of property graph languages running against
RDF graphs
Enterprise architects tend to like RDF graphs, developers
often prefer property graphs, so you can please both
constituencies
Combine the graph traversal capabilities of property graphs
with the inferencing provided by RDF graphs

13. Confidential © Bloor Research 2020 Evolution is Essential
Knowledge graphs
How does this differ
from a regular graph?
Why can’t I use a
relational approach?

14. Confidential © Bloor Research 2020 Evolution is Essential
Data federation/virtualisation
The flip side of knowledge graphs:
KGs enable data virtualisation, DV enables knowledge graphs

15. Confidential © Bloor Research 2020 Evolution is Essential
Making life easy
Languages
– Increasing of use IDEs to hide language complexities, especially
proprietary languages
– GQL now taken on by ANSI (1st non-SQL)
– GraphQL also to hide underlying language
Increased provision of migration tools, either from other
graph databases or relational databases

16. Confidential © Bloor Research 2020 Evolution is Essential
Summary
Graph market continues to grow
Use cases expanding with increased capabilities to
support geospatial and machine learning
Increased scalability to meet enterprise needs
More focus being put on making graph technology
easier to sue, both for developers and end users

17. Graph
Algorithms
Increasing Data for Analytics Volumes
Transactions
(GOLTP)
Query Scalability
of short, fast,
targeted
queries
Real-time
Transactions.
Many users.
Real-time
Analytics on
Small Data Set
Volumes
Real-time Complex
Analytics queries on
Small and Large
Data Set Volumes
Data Integration &
data transformations
(ELT and Feature
Engineering)
Warehouse
(GOLAP)
Graph
Algorithms on
Large Data
Volumes
Custom Graph
Algorithms.

18. Chat Bloor Research & Cambridge Semantics
Enter your questions for Philip and Sean in the Q&A panel.

19. Thanks! AnzoGraph.com
Download the Free Edition