Steve markey is the
principal of nControl, a
consulting firm based in
Philadelphia, Pennsylvania,
USA. He is also an adjunct
professor and the current
president of the Delaware
Valley (Greater Philadelphia)
chapter of the Cloud Security
Alliance (CSA). Markey holds
multiple certifications and
degrees, and has more
than 11 years of experience
in the technology sector.
He frequently presents
on information security,
information privacy,
cloud computing, project
management, e-discovery
and information governance.
A Primer on Nonrelational, Distributed
Databases for IS Professionals
A new paradigm is among us. Along with the cloud
and mobile devices, nonrelational, distributed
database management systems (non-RDBMS)
are now growing in popularity. Examples of
these database implementations include NoSQL
(not only SQL) offerings such as Cassandra,
CouchDB, FlockDB, GraphDB, Hibari, MongoDB
and SimpleDB. Although non-RDBMS have
been around for the better part of a decade,
their popularity has grown to the point at which
information systems professionals should become
familiar with their architecture and use, how
to assess their risk posture, and how to secure
them. This article provides an overview of this
technology, an idea of the security vulnerabilities
inherently found in these technologies and
guidance on how to remediate those vulnerabilities.
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nOn-rDbmS vALue-ADD
Once thought of as a technology solely for
academia, non-RDBMS are now reaching critical
mass in industry. Leading technology service
providers (e.g., Twitter) have begun to use
them, and individuals and companies consume
those provider offerings. 1 Non-RDBMS are
becoming the preferred database architecture for
organizations using Web 2.0 technologies due
to the open-source nature of these platforms,
which leads to cost savings because organizations
do not have to invest in traditional relational
database software licensing and/or local
hardware. Concurrently, large enterprises are
using non-RDBMS to augment their existing
RDBMS investments for storing and analyzing
big data. Big data, as defined through Oracle,
includes the aggregation of an organization’s
traditional, sensory (e.g., log data, metadata)
and social (media) data. 2 Beyond cost savings,
organizations can expect to experience enhanced
scalability, elasticity (called sharding3 in the
non-RDBMS world), modularity, portability
and interoperability while using non-RDBMS
platforms with Web 2.0 technologies, such as
Ruby on Rails (a Web 2.0 programming language
focused on dynamic content) or web services/
service-oriented architecture (SOA).
