Advanced Databases and Knowledge Management

The First Step in Information Management
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Advanced Databases and Knowledge Management
September 6, 2018

Welcome to Today’s Discussion
 Overview of database management systems (DBMS) technologies
 Scope of current DBMS technologies
− Graph AND OTHER No SQL (non-Hadoop)
 Analytics use cases
 Knowledge management and future usage
 Best practices and key takeaways
 Q&A
pg 2© 2018 First San Francisco Partners www.firstsanfranciscopartners.com

Overview of DBMS Technologies
 A majority of organizations today have
heterogeneous solutions for analytics.
 In-house DBMS (vendor/architecture still
important) vs. Cloud (vendor/architecture
not as important).
 No longer single model to multiple use case.
 Evolving from DBMS as the global data store
to technical requirement driven.
A database management
system (DBMS) is system
software for creating and
managing databases. The
DBMS provides users and
programmers with a
systematic way to create,
retrieve, update and
manage data.
- TechTarget SearchSQLServer

“New Types” of DBMS
Old View
•Hierarchical
•Network
•Relational
•Object-oriented
Expanded
View
•Columnar
•Graph
“New” View
•No SQL (key-
value, wide
column, graph, or
document)
•Appear like
original view PLUS
specialized
functionality
Realistic View
•Types and
functions are
blurred
•e.g. some RDBMS
have graph
capability
The emphasis is on the role the DBMS performs.
The era of the dedicated single enterprise DBMS is over.

Progression of DBMS Emphasis and Scope
Mathematical
Model
Graph
Inverted list
B-tree
Relational
Storage
Graph
HDFS
Columnar
Time Value
Architecture
Hadoop
Relational
Hierarchical
Columnar
Graph
Use case
Transactional
Content
management
Structured data
analysis
Advanced analytics
Lineage and
knowledge mapping
Visualization

Mathematical
Model
Graph
Inverted list
B-tree
Relational
Storage
Graph
HDFS
Columnar
Time Value
Architecture
Hadoop
Relational
Hierarchical
Columnar
Graph
Use case
Transactional
Content
management
Structured data
analysis
Advanced analytics
Lineage and
knowledge mapping
Visualization
Progression of DBMS Scope

About Graph Databases
 A Graph database is a database
designed to treat the relationships
between data as equally important
to the data itself.
 Less emphasis on a pre-defined
model for structure.
 Data is “stored” like we first
draw it out – showing how each
individual entity connects with
or is related to others.
 Think of complicated many-to-
many-to-many relationships.
PERSON
Name:John
PERSON
Name:Martha
Married
Year:1982
Node
Label
Property
Relationship
Name
Property

8
Interesting Or Boring?
Senior Executives
Mr. Smith
Ms. Jones
Mr. Subramanian
…
…
…
Copyright SingerLinks Consulting LLC 2017 www.singerlinks.com

9
IPV4
20.5.100.10
20.5.100.11
20.5.100.12
20.5.101.10
20.5.101.11
…
Senior Executives
Mr. Smith
Ms. Jones
Mr. Subramanian
…
…
…

10
IPV4
20.5.100.10
20.5.100.11
20.5.100.12
20.5.101.10
20.5.101.11
…
Senior Executives
Mr. Smith
Ms. Jones
Mr. Subramanian
…
…
…
It’s the relationships that are interesting – what if this result is from a security audit?

11
The Path from Boring to Interesting Graph
Exec
Business
Capability
Application
System
Computer
System
L2
Interface
IPV4
JBOSS
OWNS
SUPPORTS EXECUTES ON
RUNSON
CONFIGURES
BOUND
EA TOOL APM Tool IT ASSET (CMDB Tool) NETWORK SCANNER
Answering difficult questions typically requires many hops through multiple
domains of data. This data probably exists, but is not linked together.

Multiple DBMS Technology Usage
Data Lake
Graph
Query
Metadata and Lineage via Knowledge Map
Update
Metadata
Data Uses
Processes
Data
Changes
Analytic
Model
Data Data

NoSQL DBMS
KEY-VALUE
WIDE
COLUMN
DOCUMENT
NON-
HADOOP
NO-SQL
Project
Voldemort
Amazon
Dynamo
MongoDB*
*MongoDB is also a
key-value and wide
column solution

DBMS Usage in Analytics
 DBMS technology needs to cover:
Functionality of
Data Warehouse
Virtualization or
Logical Data
Warehouse
Real time analytics
Context
Independent
Multiple formats
Lineage, metadata,
and mapping of
massive amounts
of diverse content
Foundation for AI
Structured data
analysis
Advanced analytics Visualization

Varied DBMS Usage in Analytics — Sample Architecture
 Graph for knowledge
mapping and metadata
 Document data base for
document storage and use
 Hadoop or other NoSQL for
merging and analyzing
varied content
 Columnar for handling
Vintage area BI and
Reporting
Contemporary Area
1
Data Life
Cycles
Data Management
Data Usage
Vintage Area
Legacy BI and Reporting
Data Warehouse, ODS,
Mart
ETL,
EAI,
Msg,
Copy
Data Lake
Hadoop
Advanced Analytics
RDBMS, SQL,
Columnar, Transactional
Metadata
Logical DW
Data Sources
Knowledge
Graph
BIVisualization
Document

Knowledge Management and Future Usage
“ Knowledge Management turns
the potential capacity of raw
“connected and collaborative
intelligence”, i.e. all those brains
at the end of the computer, into
a “collective know-how” that will
improve operations,
competitiveness and value. ….. It
is a SUM of information assets,
…and most importantly, the un-
captured, tacit expertise and
experience resident in the
minds of people.”
“ Knowledge management is
a discipline that promotes an
integrated approach to
identifying, capturing,
evaluating, retrieving, and
sharing all of an enterprise's
information assets. ... The
one real lacuna of this
definition is that it, too, is
specifically limited to an
organization's own
information and knowledge
assets. “
 The context,
metadata and
the relationships
are as important
as the values of
the records.
John Ladley Wikipedia

Knowledge Management and Future Usage
 Blurs with AI and machine learning
 Still retains old challenges that AI needs
to take to heart (data quality/data
movement/context)
 When you present a sophisticated
model, whether derived from
exploration or a recognized pattern,
you still need to apply what people
ALREADY KNOW
FutureAnalytics
Knowledge
Management
Machine Learning
Artificial Intelligence
Well Managed Data
Supply Chain

DBMS Will Support Organizational Learning
SQL
No
SQL
Hierarchical
No
SQL
No
SQL
AI / Analytics Models
Conclusion
LEARNING
AI “closed
loop” rule
Knowledge
Graph
SQL
LEARNING CAPTURED
LEARNING
ACTION

Best Practices
 Understand your data and usage
 Determine what the need is for
specialized DBMS
 Test your current DBMS stack through a
POC to see if the merchant vendor can
really handle the task.
 Understand data quality or business
needs

Key Takeaways
KEEP IN MIND…
 Do you know what capabilities you are trying to enable?
 Do you know business latencies ?
 Are you looking at native or conceptual aspects for database use cases?
 Are you keeping track of the vendors?
 Can you manage and afford many DBMS or do you need to work hard with
one or two large players?
 You can’t buy one of everything

Please Share Your Questions and Comments
MONTHLY SERIES

Thank you for joining us today!
Our Thursday, October 4
#DIAnaltyics webinar is:
Lessons Learned From Building a
Data Supply Chain
.
John Ladley @jladley
john@firstsanfranciscopartners.com
Kelle O’Neal @kellezoneal
kelle@firstsanfranciscopartners.com

Advanced Databases and Knowledge Management

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