Databases Reference
In-Depth Information
End Points
The big question for enterprises with growing big data analytics investments is whether
to choose an RDBMS-only solution, or a dual RDBMS and map-reduce/Hadoop solution.
Over time, the two architectures will not exist as separate islands but rather will have rich
data pipelines going in both directions. It is safe to say that both architectures will evolve
hugely over the next decade.
Sometimes when an exciting new technology arrives, there is a tendency to close the
door on older technologies as if they were going to go away. Traditional data warehousing
has built an enormous legacy of experience, best practices, supporting structures,
technical expertise, and credibility with the business world. This will be the foundation
for information management in the upcoming decade as data warehousing expands to
include big data analytics.
A next-generation data architecture is emerging that connects the classic systems
powering business transactions and interactions with Hadoop, a hybrid architecture
capable of storing, aggregating, and transforming multi-structured raw data sources into
usable formats that help fuel new insights for the business. The unprecedented growth
and availability of data across a diverse set of channels and the competitive advantage
that organizations gain from harnessing that data are the key driving factors for big
data adoption. Hadoop's ability to run on commodity servers, store a broad range
of data types, process analytic queries via map-reduce and predictably scale with
increased data volumes are very attractive solution characteristics as it pertains to big
data analytics. RDBMS based EDW solutions such as
Netezza
and
Greenplum
appliances
enable low latency access to high volumes of data, provide data retrieval via SQL,
integrate with a wide variety of enterprise BI and ETL tools and are optimized for
price/performance across a diverse set of workloads. Organizations that architect their
big data platforms integrating the two technologies have the ability to take advantage of
the best of both worlds.
Big data analytics is a computational discipline and one would need to skillfully
architect multiple technologies to meet its broad objectives. It's disruptive in nature and
would pose architectural challenges to IT organizations similar in scale as SOA in the
late 1990s and cloud computing over the last decade. Organizations that overcome those
challenges and use the right set of technologies for big data analytics will be successful.
References
Big Data: Hadoop, Business Analytics and Beyond: Jeff Kelly Nov 08, 2012:
CAP Twelve Years Later: How the "Rules" Have Changed: Eric Brewer on May 30, 2012 -
Key Value Database:
bigdatanerd.wordpress.com
NoSQL Databases:
www.newtech.about.com
DataWarehouseBigDataAnalyticsKimball.pdf:
Big Data Diversity Meets EDW Consistency for New Synergies in BI: Nancy McQuillen,
2 December 2011
