As the world works it way through a "once in a generation"
economic crisis, the long overdue downgrade of the RDBMS, from its pivotal position at the
apex of the data access and data management pyramid is
What is the Data Access, and Data Management Value
As depicted below, a top-down view of the data access and data
management value chain. The term: apex, simply indicates value
primacy, which takes the form of a data access API based entry
point into a DBMS realm -- aligned to an underlying data model.
Examples of data access APIs include: Native Call Level Interfaces
(CLIs), ODBC, JDBC, ADO.NET, OLE-DB, XMLA, and Web Services.
See: AVF Pyramid Diagram.
The degree to which ad-hoc views of data managed by a DBMS can
be produced and dispatched to relevant data consumers (e.g.
people), without compromising concurrency, data durability, and
security, collectively determine the "Agility Value Factor" (AVF)
of a given DBMS. Remember, agility as the cornerstone of
environmental adaptation is as old as the concept of evolution, and
intrinsic to all pursuits of primacy.
In simpler business oriented terms, look at AVF as the degree to
which DBMS technology affects the ability to effectively implement
"Market Leadership Discipline" along the following pathways:
innovation, operation excellence, or customer intimacy.
Why has RDBMS Primacy has Endured?
Historically, at least since the late '80s, the RDBMS genre of
DBMS has consistently offered the highest AVF relative to other
DBMS genres en route to primacy within the value pyramid. The
desire to improve on paper reports and spreadsheets is basically
what DBMS technology has fundamentally addressed to date, even
though conceptual level interaction with data has never been its
See: RDBMS Primacy Diagram.
For more then 10 years -- at the very least -- limitations of
the traditional RDBMS in the realm of conceptual level interaction
with data across diverse data sources and schemas (enterprise, Web,
and Internet) has been crystal clear to many
RDBMS technology practitioners, as indicated by some of the quotes
"Future of Database Research is excellent, but what is the
future of data?"
"..it is hard for me to disagree with the conclusions in this
report. It captures exactly the right thoughts, and should be a
must read for everyone involved in the area of databases and
database research in particular."
-- Dr. Anant Jingran, CTO, IBM Information Management Systems, commenting on
the 2007 RDBMS technology retreat attended by a
number of key DBMS technology pioneers and researchers.
"One size fits all: A concept whose time has come
They are direct descendants of System R and Ingres and were architected more than 25
They are advocating "one size fits all"; i.e. a single
engine that solves all DBMS needs.
-- Prof. Michael Stonebreaker, one of the founding
fathers of the RDBMS industry.
Until this point in time, the requisite confluence of
"circumstantial pain" and "open standards" based technology
required to enable an objective "compare and contrast" of RDBMS
engine virtues and viable alternatives hasn't occurred. Thus, the
RDBMS has endured it position of primacy albeit on a "one size fits
As mentioned earlier, we are in the midst of an economic crisis
that is ultimately about a consistent inability to connect dots
across a substrate of interlinked data sources that transcend
traditional data access boundaries with high doses of schematic
heterogeneity. Ironically, in a era of the dot-com, we haven't been
able to make meaningful connections between relevant "real-world
things" that extend beyond primitive data hosted database tables
and content management style document containers; we've struggled
to achieve this in the most basic sense, let alone evolve our
ability to connect inline with the exponential rate at which the Internet & Web
are spawning "universes of discourse" (data spaces) that emanate
from user activity (within the enterprise and across the
Internet & Web). In a nutshell, we haven't been able to upgrade
our interaction with data such that "conceptual models" and
resulting "context lenses" (or facets) become concrete;
by this I mean: real-world entity interaction making its way into the
computer realm as opposed to the impedance we all suffer today when
we transition from conceptual model interaction (real-world) to
logical model interaction (when dealing with RDBMS based data
access and data management).
Here are some simple examples of what I can only best describe
as: "critical dots unconnected", resulting from an inability to
interact with data conceptually:
Government (Globally) -
Financial regulatory bodies couldn't effectively discern that a
Credit Default Swap is an Insurance policy in
all but literal name. And in not doing so the cost of an
unregulated insurance policy laid the foundation for
exacerbating the toxicity of fatally flawed mortgage backed
securities. Put simply: a flawed insurance policy was the fallback
on a toxic security that financiers found exotic based on
Banks still don't understand that capital really does exists in
tangible and intangible forms; with the intangible being the
variant that is inherently dynamic. For example, a tech companies
intellectual capital far exceeds the value of fixture, fittings,
and buildings, but you be amazed to find that in most cases this
vital asset has not significant value when banks get down to the
nitty gritty of debt collateral; instead, a buffer of flawed
securitization has occurred atop a borderline static asset class
covering the aforementioned buildings, fixtures, and fittings.
In the general enterprise arena, IT executives continued to "rip
and replace" existing technology without ever effectively
addressing the timeless inability to connect data across disparate
data silos generated by internal enterprise applications, let alone
the broader need to mesh data from the inside with external data
sources. No correlations made between the growth of buzzwords and
the compounding nature of data integration challenges. It's 2009
and only a miniscule number of executives dare fantasize about
being anywhere within distance of the: relevant information at your
Looking more holistically at data interaction in general,
whether you interact with data in the enterprise space (i.e., at
work) or on the Internet or Web, you ultimately are delving into a
mishmash of disparate computer systems, applications, service (Web
or SOA), and databases (of the RDBMS variety in a majority of
cases) associated with a plethora of disparate schemas. Yes, but
even today "rip and replace" is still the norm pushed by most
vendors; pitting one mono culture against another as exemplified by
irrelevances such as: FOSS/LAMP vs Commercial or Web vs.
Enterprise, when none of this matters if the data access and
integration issues are recognized let alone addressed (see:
Applications are Like Fish and Data Like
Like the current credit-crunch, exponential growth of data
originating from disparate application databases and associated
schemas, within shrinking processing time frames, has triggered a
rethinking of what defines data access and data management value
today en route to an inevitable RDBMS downgrade within the value
There have been many attempts to address real-world modeling
requirements across the broader DBMS community from Object
Databases to Object-Relational Databases, and more recently the
emergence of simple Entity-Attribute-Value model DBMS engines. In
all cases failure has come down to the existence of one or more of
the following deficiencies, across each potential alternative:
- Query language standardization - nothing close to SQL
- Data Access API standardization - nothing close to ODBC, JDBC,
OLE-DB, or ADO.NET
- Wire protocol standardization - nothing close to HTTP
- Distributed Identity infrastructure - nothing close to the
non-repudiatable digital Identity that foaf+ssl accords
- Use of Identifiers as network based pointers to data sources -
nothing close to RDF based Linked Data
- Negotiable data representation - nothing close to Mime and HTTP
based Content Negotiation
- Scalability especially in the era of Internet & Web
Entity-Attribute-Value with Classes & Relationships
(EAV/CR) data models
A common characteristic shared by all post-relational DBMS
management systems (from Object Relational to pure Object) is an
orientation towards variations of EAV/CR based data models.
Unfortunately, all efforts in the EAV/CR realm have typically
suffered from at least one of the deficiencies listed above. In
addition, the same "one DBMS model fits all" approach that lies at
the heart of the RDBMS downgrade also exists in the EAV/CR
What Comes Next?
The RDBMS is not going away (ever), but its era of primacy -- by
virtue of its placement at the apex of the data access and data
management value pyramid -- is over! I make this bold claim for the
- The Internet aided "Global Village" has brought "Open World" vs "Closed World" assumption issues to the fore
e.g., the current global economic crisis remains centered on the
inability to connect dots across "Open World" and "Closed World"
- Entity-Attribute-Value with Classes & Relationships
(EAV/CR) based DBMS models are more effective when dealing with
disparate data associated with disparate schemas, across disparate
DBMS engines, host operating systems, and networks.
Based on the above, it is crystal clear that a different kind of
DBMS -- one with higher AVF relative to the RDBMS -- needs to sit
atop today's data access and data management value pyramid. The
characteristics of this DBMS must include the following:
- Every item of data (Datum/Entity/Object/Resource) has
- Identity is achieved via Identifiers that aren't locked at the
DBMS, OS, Network, or Application levels
- Object Identifiers and Object values are independent
(extricably linked by association)
- Object values should be de-referencable via Object
- Representation of de-referenced value graph (entity,
attributes, and values mesh) must be negotiable (i.e. content
- Structured query language must provide mechanism for Creation,
Deletion, Updates, and Querying of data objects
- Performance & Scalability across "Closed World"
(enterprise) and "Open World" (Internet & Web) realms.
Quick recap, I am not saying that RDBMS engine technology is
dead or obsolete. I am simply stating that the era of RDBMS primacy
within the data access and data management value pyramid is
The problem domain (conceptual model views over heterogeneous
data sources) at the apex of the aforementioned pyramid has simply
evolved beyond the natural capabilities of the RDBMS which is
rooted in "Closed World" assumptions re., data definition, access,
and management. The need to maintain domain based conceptual
interaction with data is now palpable at every echelon within our
"Global Village" - Internet, Web, Enterprise, Government etc.
It is my personal view that an EAV/CR model based DBMS, with
support for the seven items enumerated above, can trigger the long
anticipated RDBMS downgrade. Such a DBMS would be inherently
multi-model because you would need to the best of RDBMS and EAV/CR
model engines in a single product, with in-built support for HTTP
and other Internet protocols in order to effectively address data
representation and serialization issues.
EAV/CR Oriented Data Access & Management Technology
Examples of contemporary EAV/CR frameworks that provide concrete
conceptual layers for data access and data management currently
The frameworks above provide the basis for a revised AVF
pyramid, as depicted below, that reflects today's data access and
management realities i.e., an Internet & Web driven global
village comprised of interlinked distributed data objects,
compatible with "Open World" assumptions.
See: New EAV/CR Primacy Diagram.