MemSQL Presentation from Gartner Data and Analytics Summit 2018

1. Building a
Machine Learning
Recommendation Engine
in SQL
@garyorenstein @memsql
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2. Today’s Talk
1. State of Data 2018 according to Gartner
2. Rise of Machine Learning
3. Live Demo - A SQL Recommendation Engine
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3. SECTION 1
The State of DataAccording to Gartner 2018
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4. Hype Cycle for Data
Management
26 July 2017
Donald Feinberg
Adam M. Ronthal
G00313950
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5. MemSQL 5

6. Multimodel has the potential
to support both relational and
nonrelational use cases
while reducing the number of
disparate DBMS products
in an organization.
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7. the idea of a
Hadoop distribution
will become obsolete
before it reaches
the Plateau of Productivity
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8. Penetration continues to increase and organizations
should be evaluating these resources for
— cost-efficiency
— infrastructure simplification and
— new use cases, such as Hybrid Transactional/
Analytical Processing (HTAP)
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9. Build Your Digital Business
Platform Around Data and
Analytics
31 January 2018
Andrew White
W. Roy Schulte
Roxane Edjlali
Joao Tapadinhas
Svetlana Sicular
G00350435
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10. Select Challenges
Data and analytics investments that are tied to
measurable business outcomes are more likely to
produce reportable benefits.
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11. Magic Quadrant for Data
Management Solutions for
Analytics
13 February 2018
Adam M. Ronthal
Roxane Edjlali
Rick Greenwald
G00326691
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12. We define four primary use cases for DMSAs that reflect
this diversity of data and use cases:
— Traditional data warehouse
— Real-time data warehouse
— Context-independent data warehouse
— Logical data warehouse
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15. Real-Time Data Warehouse
This use case adds a real-time component to analytics
use cases, with the aim of reducing latency — the time
lag between when data is generated and when it can be
analyzed.
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17. Other Vendors to Consider for
Operational DBMSs
23 November 2017
Donald Feinberg
Merv Adrian
Nick Heudecker
G00327284
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18. Other Vendors to Consider for Operational DBMSs
Actian
Aerospike
Alibaba Cloud
Altibase
ArangoDB
Cloudera
Clustrix
Couchbase
FairCom
Fujitsu
General Data Technology
Hortonworks
MariaDB
MemSQL
MongoDB
Neo4j
NuoDB
Percona
Redis Labs
SequoiaDB
TmaxSoft
VoltDB
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19. Other Vendors to Consider for Operational DBMSs
also listed as Challenger or Leader
in the Magic Quadrant
for Data Management Solutions for Analytics
MemSQL
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21. Over the next five years,
the OPDBMS and DMSA
markets converge to a
single DBMS market.
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22. Look to your operational DBMS
vendor for both transactional
and analytical workloads.
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23. SECTION 2
Rise of Machine Learning
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30. 2018 Outlook Survey
MemSQL and O’Reilly
1600+ respondents
memsql.com/MLsurvey
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33. Machine Learning and
Databases
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48. SECTION 3
DEMO with Yelp Dataset
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53. Can you build a machine
learning recommendation
engine in SQL?
Yes
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54. Can you build a machine learning
recommendation engine in SQL?
Yes
Should you?
For training? Maybe, maybe not.
For Operational Scoring?
Absolutely!
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57. Secret Weapons to Machine Learning in SQL
— Extensibility
— Stored Procedures
— User Defined Functions
— User Defined Aggregates
— DOT_PRODUCT
— Compare two vectors
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60. Sequel Pro Mac app for MySQL databases
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61. MemSQL in one slide
— Distributed SQL database
— Massively parallel, lock-free, fast
— Full ACID features
— In-memory and on-disk
— JSON, key-value, geospatial, full-text search
— Robust security
— Built for transactions and analytics
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64. Why do ML in SQL?
— Train in any number of systems
— Score in the database for applications from real-time
drilling to fraud detection to personalization
— Complete certain functions within the database to
radically simplify operational infrastructure
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65. “It is a fine line between
a well executed SQL query on
live data and ML/AI”
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66. MemSQL 66

67. Thank you!
Please visit our booth
www.memsql.com
@garyorenstein
@memsql
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68. Abstract: Building a Machine Learning Recommendation Engine in SQL
Modern businesses constantly seek deeper customer relationships and more
compelling experiences.
To accomplish this, companies are looking to machine learning and artificial
intelligence solutions; however, that often involves a host of new systems and
approaches.
With a modern database architecture, it is possible to build compelling machine
learning solutions with SQL, deliver real-time engagements, and rapidly move to
operational applications.
See live, how a modern database can accomplish these feats within a single
integrated solution.
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