Roundtable 1: Relational and Analytic Database Innovations

Eric Kavanagh
Eric.kavanagh@bloorgroup.com

Twitter Tag: #briefr
Wednesday, February 22, 12

To conduct an Open Research program that
invites the participation of both IT users and
technology vendors

To assist IT buyers in understanding database
technology and the architecture that surrounds
it.

Allow audience members to pose serious
questions... and get answers!

Publish all findings


Your Host: Eric Kavanagh
Research Leader: Mark Madsen - Third Nature
Primary Collaborator: Robin Bloor - The Bloor
Group
Guest Analyst 1: Rajiv Rawat - BI Results
Guest Analyst 2: Malcolm Chisholm - Consultant


Rajeev Rawat is the founder and CEO of BI
Results. His career has involved leading large
cross-functional teams at both IBM and Xerox,
where he was involved in direct customer facing
roles as well as taking part in headquarters
assignments.
His headquarters positions with worldwide
responsibility included strategic assignments for
alliances and relationships with technology
partners, product management and product
marketing. Other responsibilities include
restructuring business models, test of new
technology platforms, and sales coverage plans.
Rajeev led the introduction of new technologies
and solutions for Xerox and IBM.
www.biresults.com, biresult@gmail.com
LinkedIn: Rajeev Rawat

Twitter Tag: #brief

The Bloor Group

Fit for Purpose: The New Database Revolution
The Bloor Group – February 22, 2012

Five Years of Incredible Excitement In
Information Acrobatics!

-Seismic shift in data

Ç Ç
Variety, Volume, Velocity

√ √

Rajeev Rawat
Serving to achieve your full potential
©Copyright BI Results, LLC 2012


The Bloor Group

The Next Five Years
The Most Exciting Times In Information Acrobatics

New Venture Funding
Key Value Store, Big Table, Graph DB, Document DB
New (Needed) Functionality

Ç Ç
New Skills

√ √
New Ventures

Innovative Code
NoSQL Innovation
Lots of Great Innovation Apache Project, Amazon, Facebook,
Google, Open Source Community, Twitter

Reports of the Death of The RDBMS
Are Highly Exaggerated


The Bloor Group

RDBMS Still Dominates
Reliable Heavy Lifting

RDBMS Vs. NoSQL?

Strengths
- Robust (ACID, Fail-proof)
- Structure (Granular, Scalable, Fast)
Ç - Governance (Backups, Precision)
- Tools (ETL, Analytics, Reporting)
Ç
√ - Ecosystem (Global deep collaboration)
- Skills (Certiﬁcations, Experience)
√
- Policies, Procedures (Reliability)
- Documentation (Support, Training)
Photo: Watchmojo.com

Reports of the Death of RDBMS
Are Highly Exaggerated


The Bloor Group

NoSQL
Being Tested, Validated, Calibrated
Key Value Store, Big Table, Graph
- Co-Existence, Transition, DB, Document DB
NoSQL Only

- Meta Tag, Master Data

Ç Ç
Other scheme/s

√ √
- Data Governance, Controls.
Authentication, Security

- Deep Analytics on Mixed
Datasets
Complexity, Semi- Structured,
Highly Connected Data

Fantastic Growth Opportunity
Skills, Investing


The Bloor Group

NoSQL, RDBMS Innovation
Fantastic Opportunity for Growth

Gaps You Can Help Close
The Race Is On!
- Mapping Big Data with
Legacy Data

Ç - Strategy and Policy for Finish Line
Ç
√ √
Governance, Precision,
Controls

- Opportunities at all sides
- Enterprise
- Legacy Vendors
- Innovative Ventures Tested For Prime Time
- Technology and Business
Time to Rise To The Top
Skills, Investing


Disection &
Discussion


Robin Bloor is Chief
Analyst at The
Bloor Group.

Robin.Bloor@Bloorgroup.com


RDBMS


The SQL Barrier
SQL has:
DDL (for data deﬁnition) SQL
Barrier
DML (for Select, Project and Join)
Results Or results
But it has no MML or TML processing
must be done here
processing
must be done here
Usually result sets are brought to the
client for further manipulation, but
using them for further data access
becomes problematic. SQL

Conclusions: Analytic
DBMS
This separation of data from process
is arbitrary and unhelpful


That MapReduce Thing
There are two fundamental
approaches to parallelism
Data Partitioning
Process partitioning
MapReduce implements an
approach which is oriented to
the ﬁrst of these. Thus proves
to be suited to many “big data”
tasks.
It is not the end ofd the parallel
processing story by any means.


Malcolm Chisholm has 25+ years experience in
data management working in finance, insurance,
manufacturing, government, defense,
pharmaceuticals, and retail. He specializes in
data governance, MDM, metadata engineering,
business rules management/execution, data
architecture and design. He is a well-known
presenter at conferences in the U.S. and Europe,
writes columns in trade journals, and has
authored the books: Managing Reference Data in
Enterprise Databases; How to Build a Business
Rules Engine; and Definitions in Information
Management. In 2011, Malcolm was presented
with the prestigious DAMA International
Professional Achievement Award for
contributions to Master Data Management.
He can be contacted at
mchisholm@refdataportal.com.


The New Database Revolution:
Relational Roundtable

The Virtual Circle

February 22, 2012
San Francisco

Malcolm Chisholm Ph.D.
mchisholm@refdataportal.com
Telephone 732-687-9283 • Fax 407-264-6809
www.refdataportal.com
www.bizrulesengine.com

© AskGet.com Inc., 2012. All rights reserved


“Big Data” Is Used Differently
Relational Paradigm ULS Dataspace in Cloud

“Set at a time” processing Uncover individual facts

Behavior of populations of identical things Much is master data

Event data predominates Events are not as much repetitive transactions
Exception reporting for singular things/events Can aggregate from individual facts (but bottom-
(bust still top-down) up)
Heavy data entry supported Surf and drill

Data entry is to support analysis

• The relational paradigm is different to ULS “Big Data”. [ULS = Ultra-Large
Scale - usually Petabyte scale]
• Difficult to rely on relational thinking in Cloud databases


Sources
Source A
Emails
Source B Documents I ULS Dataspace in Cloud

Web Pages N
Source C G
XML E
Source D Relational S
T
Flat Files I
Source E O
Audio
N
Image
Video
• Sources provide data to the ULS dataspace
• One source can provide many data formats
• Many sources can provide the same format
• Sources may duplicate the same data
• HINT – Think metadata

Segments in Dataspace
ULS Dataspace in Cloud
Source A I
N Ingested Data Terms in
G Store Documents Document-Term
Source B E Inverted Index
S M/R M/R
Source C T
I
O
Source N N
Extracted Deduplicated
Master Data Master Data

M/R M/R

• The ULS dataspace is not a single “blob” of data
• It will have different segments with different kinds of data in it
• The segments will be derived from the originally ingested data
• MapReduce (M/R) is the equivalent of ETL to move data around and
transform it (filter, summarize)


No Common Notation for Columnar Designs

?

Col A Col B Col C Col D Col E
Row 01 Val1A
Row 02 Val2A Val2B Val2C Val2D Val2E
Row 03 Val3A Val3C Val3E

• E/R diagramming techniques allow us to visualize a relational database
• There is nothing that is quite the same for columnar databases
• (a) It is sparse and columns may be missing
• (b) How do you show the MapReduce transformations (not quite relations)?



Need a Data Dictionary

• The ULS dataspace can grow quickly and have many data objects
• Without a DD developers and users will get hopelessly lost (none of the
logic imposed by the relational model)
• The fundamental unit is the field – show where it occurs in rows, ColQuals
and payloads
• Tables less important than in relational


Mark Madsen is founder of Third Nature, a
research and consulting firm focused on
analytics, BI and decision-making. Mark
spent the past two decades working on
analysis and decision support in many
industries and countries. He is an award-
winning architect and former CTO whose
work has been featured in numerous
industry publications. Over the past ten
years Mark received awards for his work
from the American Productivity & Quality
Center, TDWI, and the Smithsonian Institute.
He is an international speaker, a contributing
editor at Intelligent Enterprise, and manages
the open source channel at the Business
Intelligence Network. For more information
or to contact Mark, visit http://
ThirdNature.net.


One Size Doesn’t Fit All

February 22, 2012

Mark R. Madsen
http://ThirdNature.net


The
future
of
data
is
the
database


You keep using that word.
I do not think it means
what you think it means.


Good
conceptual
model,
but
a
prematurely

standardized
implementa5on

The
rela*onal
database
is
the
franchise
technology
for
storing
and

retrieving
data,
but…
1.Global,
sta*c
schema
model
2.No
rich
typing
system
3.Many
are
not
a
good
ﬁt
for
network
parallel
compu*ng,
aka
cloud
4.Limited
API
in
atomic
SQL
statement
syntax

&
simple
result
set
return


Plus,
if
they’re
all
the
same
why
are
there
so
many?
Sybase
IQ,
ASE EnterpriseDB
Algebraix
Teradata,
Aster
Data LucidDB Intersystems
Caché
Oracle,
RAC Vectorwise Streambase
MicrosoT
SQLServer,
PDW MonetDB SQLStream
IBM
DB2s,
Netezza Exasol Coral8
Paraccel Illuminate Ingres
Kogni*o Ver*ca Postgres
EMC/Greenplum InﬁniDB Cassandra
Oracle
Exadata 1010
Data CouchDB
SAP
HANA SAND Mongo
Infobright Endeca Hbase
MySQL Xtreme
Data Redis
MarkLogic IMS RainStor
Tokyo
Cabinet Hive Scalaris

And a few hundred more.

The
future
of
data
is
the
rela0onal
database?

SQL noSQL


Technologies
are
not

perfect
replacements
for

one
another.

When
replacing
the
old

with
the
new
(or
ignoring

the
new
over
the
old)
you

always
make
tradeoﬀs,
and

usually
you
won’t
see
them

for
a
long
0me.


March:
Vendor Research
March 14th: Second Round Table focusing on No SQL databases and
their application
DB Revolution Survey conducted

April:
Vendor Research
Publishing of Round Table Transcripts, with comments
May:
Authoring of White Paper
Publishing of White Paper
Publishing of survey activity


March 14th: Second DB
Revolution Round Table

March Briefing Room:
Integration

April Briefing Room:
Discovery

May Briefing Room: Analytics


Thank You
For Your
Attention


Roundtable 1: Relational and Analytic Database Innovations

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Roundtable 1: Relational and Analytic Database Innovations