The Briefing Room with Richard Hackathorn and Teradata
Slides from the Live Webcast on May 29, 2012
The worlds of Business Intelligence (BI) and Big Data Analytics can seem at odds, but only because we have yet to fully experience comprehensive approach to managing big data – a Unified Big Data Architecture. The dynamics continue to change as vendors begin to emphasize the importance of leveraging SQL, engineering and operational skills, as well as incorporating novel uses of MapReduce to improve distributed analytic processing.
Register for this episode of The Briefing Room to learn the value of taking a strategic approach for managing big data from veteran BI and data warehouse consultant Richard Hackathorn. He'll be briefed by Chris Twogood of Teradata, who will outline his company's recent advances in bridging the gap between Hadoop and SQL to unlock deeper insights and explain the role of Teradata Aster and SQL-MapReduce as a Discovery Platform for Hadoop environments.
For more information visit: http://www.insideanalysis.com
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3. ! Reveal the essential characteristics of enterprise
software, good and bad
! Provide a forum for detailed analysis of today s
innovative technologies
! Give vendors a chance to explain their product to
savvy analysts
! Allow audience members to pose serious questions...
and get answers!
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5. ! Analytics is, and always has been, about discovering insights
that lead to better business decisions. The range of
technologies and use cases that inhabit this area is wide:
statistical analysis, data and process mining, predictive
analytics and modeling, and complex event processing.
! What is now referred to as Big Data has pushed analytics
beyond the capabilities of traditional solutions. “Big
Analytics” has organizations diving into large heaps of data
that previously was not available or usable.
! The growing volume, variety, velocity and complexity of
data has proven to be a major challenge to organizations
who leverage analytics to maintain a competitive edge.
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6. Dr. Richard Hackathorn is a well-known
industry analyst, technology innovator
and international educator. He has
pioneered innovations in database
management, decision support and data
warehousing. Richard has published
numerous articles, presented at leading
industry conferences, and conducted
professional seminars in eighteen
countries. He has written three books,
entitled Enterprise Database
Connectivity, Using the Data Warehouse
(with William H. Inmon), and Web
Farming for the Data Warehouse.
Richard taught at the Wharton School
and at the University of Colorado.
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7. ! Teradata is known for its analytic data solutions with
a focus on integrated data warehousing, big data
analytics and business applications.
! It offers a broad suite of technology platforms and
solutions, and a wide range of data management
applications and data mining capabilities.
! Teradata features Teradata Aster is its MapReduce
platform to handle big data and big analytics on
multi-structured data.
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8. Chris Twogood is Vice President of
Product and Services Marketing for
Teradata Corporation. He is
responsible for marketing products
(database, utilities, and platform),
and services (professional and
customer services), plus technical
field sales support. Chris has twenty-
five years of experience in the
computer industry specializing in
Data Warehousing, Decision Support,
Customer Management and Appliance
platforms. Chris has held roles that
span Strategy, Application Definition,
Marketing, Product Requirements/
Management, Platform Solutions and
Product Marketing.
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27. • An interesting (and seldom discussed) facet of Big Data is the
emerging applications that are NOT social networking analytics on
web logs and website behaviors. What are the ‘killer’ apps in this
area? Do they involve the “Internet of Things”?
• Big Data is big in volume and in variety. It is also big in velocity.
There is a lot per second…per minute…per day. How should a
unifying architecture handle the velocity of Big Data?
• Many are trying to “Capture in case it is needed” as their approach
to Big Data. But, can you capture all the data? At what point does
cost of data capture/storage exceed the business benefits? How do
you decide what to capture, store, and retain?
• Data exploration is an increasingly popular term. How does it differ
from data analysis? Can you really find useful information through
data exploration when you do not know what you are looking for?
Examples?
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28. • When you unify the architecture for Big Data (as contrasted with
isolated islands of Big Data applications), the data needs to move
through several physical stores. Given the volume and velocity of
data flows, can/should Big Data be duplicated in multiple stores?
• What is the difference between the Hadoop (Hive, etc) system and
the Teradata Aster system? Could you use both for analytics? Do you
need both in your unifying architecture?
• Are the ‘traditional’ BI tools (like BusinessObjects, Cognos) relevant
to Big Data analytics? Are they needed in companies that are heavily
Big Data? Are they evolving and expanding to incorporate the new
approaches and techniques required for Big Data?
• A key requirement in any unifying Big Data architecture is managing
the complexity of schemas. It seems that we need a new generation
of semantic analysis tools to assist with schema management. What
tools are emerging to support this requirement?
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29. • Gregory Piatetsky-Shapiro of KDnuggets ran a recent poll on the
largest dataset that his audience of data miners has so far analyzed.
The median size for 2012 was in the range 10-100 GB. If most of the
data for half of the analytics projects can fit into main memory on a
server platform, why is there such a need for expensive
architectures supporting MPP, MapReduce, and the like?
• http://www.kdnuggets.com/polls/2012/largest-dataset-analyzed-
data-mined.html
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