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Exploratory Webcast for the Big Data Information Architecture Research Project Live Webcast Jan. 22, 2014 Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=32304b307fc5359a2f97b173166ea07b Big Data is everywhere -- that's for sure. But the big question for today's savvy enterprise is where, exactly, should it fit within the Information Architecture? Making that decision correctly can save a lot of money while adding significant value to any number of enterprise operations. Business processes can be improved with critical new data sets; marketing can excel at hitting the right targets quickly; sales can hit home runs by having a much deeper understanding of key prospects; and senior executives can see the big picture more clearly than ever before. Register for this Exploratory Webcast to hear veteran Analyst Dr. Robin Bloor outline the current landscape of Big Data, and offer guidance for today's organizations to determine how, when and where to deploy this powerful if unwieldy information asset. This event will kick off The Bloor Group's Interactive Research Report for 2014 which will focus on illuminating optimal Big Data Information Architectures. The series will include a dozen interviews with today's Big Data visionaries, plus three interactive Webcasts and a detailed findings report. Visit InsideAnalysis.com for more information.
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Grab some coffee and enjoy the pre-show banter before the top of the hour!
“Think Big: How to Design a Big Data Information Architecture” Exploratory Webcast | January 22, 2014
Guests
Robin Bloor Chief Analyst, The Bloor Group @robinbloor [email protected]
Eric Kavanagh CEO, The Bloor Group @eric_kavanagh [email protected]
Findings Webcast June 25, 2014
Big Data Information Architecture
Roundtable Webcast April 9, 2014
Exploratory Webcast January 22, 2014
#BigDataArch
Big Data Information Architecture
In Three Segments
The Big Data Curve?
Data Flow
Technology Disruption
PART ONE
PART THREE
PART TWO
Part 1: The Big Data Curve
The Visible “Big Data” Trend
u Corporate data volumes grow at about 55% per annum - exponentially
u Data has been growing at this rate for, maybe, 40 years
u There is nothing new about big data. It clings to an established exponential trend
The Invisible Trend: Moore’s Law Cubed
u The biggest databases are new databases
u They grow at the cube of Moore’s Law
u Moore’s Law = 10x every 6 years u VLDB: 1000x every 6 years – 1991/2 megabytes – 1997/8 gigabytes – 2003/4 terabytes – 2009/10 petabytes – 2015/16 exabytes
Technology Evolution (Bloor Curve)
The Area OfAs-Yet-Unrealized
Applications
ApplicationMigration
Source: The Bloor Group
The Traditional Force of Disruption
u Software architectures change: centralized, C/S, 3 tier/web, SOA, etc.
u Applications migrate according to latencies
u Dominant applications and software brands can die via “The innovator’s dilemma”
u Wholly new applications appear because of lower latencies, e.g., VMs, CEP
The Area OfAs-Yet-Unrealized
Applications
ApplicationMigration
Source: The Bloor Group
This Curve is Compromised
The Area OfAs-Yet-Unrealized
Applications
ApplicationMigration
Source: The Bloor Group
Two DISRUPTIVE forces have changed
the curve:
PARALLELISM and
The CLOUD
It’s not really about
Big Data???
It’s about
Part 2: Technology Disruption
It’s Over for Spinning Disk
u SSD is now on the Moore’s Law curve
u Disk is not and never was (in respect of seek time)
u All traditional databases were engineered for spinning disk and not for scale-out
u This explains the new DBMS products…
In-Memory Disruption
u Memory may gradually become the primary store for data (this impacts data flows)
u Almost all applications are poorly built for this
u Memory is an accelerator – as is CPU cache. This is becoming a factor
The Memory Cascade
u On chip speed v RAM • L1(32K) = 100x • L2(246K) = 30x • L3(8-20Mb) = 8.6x
u RAM v SSD • RAM = 300x
u SSD v Disk • SSD = 10x
Note: Vector instructions and data compression
u Computer u On-line u PC u Internet u Mobile u Internet of things
u Batch u Centralized u Client/server u Multi-tier u Service Orientation u Event Driven/Big
Data
Tech Revolutions
TECH REVOLUTION ARCHITECTURE
Event Driven/Big Data Architecture?
The Open Source Picture
u The R Language • Over 1 million
users u Hadoop and its
Ecosystem • Reduced latency
for analytics u Machine Learning
Algorithms • Raw power
None of these are engineered for performance
Part 3: Data Flow
What Is A Data Scientist?
u Project manager u Qualified statistician u Domain Business
expert u Experienced data
architect u Software engineer
(IT’S A TEAM)
A Process, Not an Activity
u Data Analytics is a multi-disciplinary end-to-end process
u Until recently it was a walled-garden. But recently the walls were torn down by…
• Data availability • Scalable technology • Open source tools
The CRITICAL Workload Issue
u Previously, we viewed database workloads as an i/o optimization problem
u With analytics the workload is a very variable mix of i/o and calculation
u No databases were built precisely for this – not even Big Data databases
Take Note
You can know more about a BUSINESS from
its data than by any other means
The Biological System
u Our human control system works at different speeds: • Almost instant reflex • Swift response • Considered response
u Organizations will gradually implement similar control systems
u This suggests a data-flow- based architecture
The Corporate Biological System
u Right now this division into two different data flows is already occurring
u Currently we can distinguish between: • Real-time/Business time
applications • Analytical applications
u We should build specific architectures for this
Some Architectural Principles
u The new atom of data is the event
u SUSO, scale up before scale out
u Take the processing to the data, if you can
u Hadoop is a component not a solution
In Conclusion
The Big Data Curve?
Data Flow
Technology Disruption
PART ONE
PART THREE
PART TWO
Questions?
#BigDataArch or
USE THE Q&A
THANK YOU!
REGISTER FOR BDIA WEBCASTS AT: http://insideanalysis.com/research/big-data-information-architecture