Turning the Corner: What It Takes to Build a Modern Data Warehouse Live Webcast October 15, 2015 Watch the archive: https://bloorgroup.webex.com/bloorgroup/onstage/g.php?MTID=e3983398e33dc035f3c66fdb6006b380d If necessity is the mother of all invention, then it’s no wonder the technology space moves so fast. From the consumerization of IT to the steady emergence of innovative solutions, organizations often struggle to stay afloat in the sea of options. As we march steady to the pace of Moore’s Law, analysts and business users expect quicker and easier access to enterprise data, and the data warehouse of yore must modernize to meet those demands. Register for this Roundtable Webcast with Analyst David Loshin, Dwaine Snow of IBM, Heine Krog Iversen of TimeXtender and TJ Laher of Cloudera. This expert panel will discuss the market forces that brought data warehousing to its current state and the key factors driving today’s innovation. They will explore how new data types and sources are raising analytic expectations and changing the way enterprises design information architectures. Visit InsideAnalysis.com for more information.

1. Grab
some
coffee and
enjoy the
pre-show
banter
before the
top of the
hour!

2. Building the Modern Data Warehouse
Roundtable Webcast | Oct. 15, 2015
SPONSORED BY

3. Welcome
Host:
Eric Kavanagh
CEO, The Bloor Group
@Eric_Kavanagh
eric.kavanagh@bloorgroup.com

4. Guests
TJ Laher
Product Marketing, Cloudera
Dwaine Snow
Strategy Lead, IBM Analytics Platforms
Heine Krog Iversen
Group CEO, TimeXtender

5. Findings Webcast
Dec. 2
The Modern Data Warehouse
Roundtable Webcast
Today, Oct. 15
Survey Under Way!
Ends Friday, Oct. 14 @ 5pm ET
#MDW

6. Data Warehousing: Past, Present, Future
Dennis Jarvis
Tibet-5809 - Yak at Yundrok Yumtso Lake
kevin ryder
old tractor
Original data warehouses were slow but powerful
Second generation offered new functionality
Third generation? Agile, Automated, Adaptive

7. Agile, Automated, Adaptive: How So?
!Agile: How quickly does the warehouse
deliver value to key stakeholders? Can
new users be onboarded efficiently?
!Automated: Does your team need to do
much manual, tedious maintance? What
about documentation?
!Adaptive: How easily can the warehouse
be modified to add new data sets?

8. Data Warehousing vs Big Data Analytics
!Data Warehousing uses structured
(relational) data to enable accurate
reporting, and facilitate ad hoc queries.
!Big Data Analytics focuses on
“unstructured” (non-relational) data to
enable business analytics.
!These are two very different paradigms,
though there is some overlap.

9. Whither, the Warehouse?
!For the time being, the Data Warehouse
will surely remain the “golden source” of
curated, governed, trusted data for key
executive decision-makers.
!Forward-thinking organizations will find
ways to combine warehouse data with
insights derived from Big Data, via use of
dashboards and data visualizations.

10. Big Value, Big Hurdles for Big Data
!Big Data offers a second chance for the
data management industry
!Because machines don’t lie, Big Data can
provide built-in accuracy (except social)
!The toolsets for harnessing Big Data
remain relatively nascent
!Though tremendously powerful, the
open-source movement is volatile

11. ©"2015"IBM"Corporation
The"Future"of"Data"
Warehousing"is"“Logical”
Dwaine'Snow
Strategy"Lead,"IBM"Analytics"Platforms
September"25th,"2015

12. ©"2015"IBM"Corporation12 IBM"Confidential
Agenda
• The Changing Nature of Data
• The Hybrid, Logical Data Warehouse
• Fluid Query – Powering Hybrid, Logical
Data Warehousing

13. ©"2015"IBM"Corporation13 IBM"Confidential

14. ©"2015"IBM"Corporation14 IBM"Confidential
The Big Data counter ( ) will aggregate all the data generated from the moment you load
the page …
This is Causing an Explosion in Data Types and Volumes

15. ©"2015"IBM"Corporation15 IBM"Confidential
Not'just'Social'Media,'
all'of'these'“new”
data'sources'–
JSON
Web'Logs
Free'form'Text
XML
CDR,'TDR
Pictures
Audio
Video
True Insight Today Requires All of Your Data Being Combined

16. ©"2015"IBM"Corporation16 IBM"Confidential
Data"Sources
Transactional
Social
Application
User"Generated
Journal
Video"and"Audio
Machine"/"Sensor
Documents
Third"Party
The Market Has Evolved into the Logical Data Warehouse
Optimizing access and reducing costs
Internal"Insight
Reporting
Enterprise"
Content
Discovery"
Exploration
Decision"
Management
Predictive
Analytics
Visualization
ExternalWFacing"
Applications
Web"or"Mobile"
Systems"of"
Engagement
Information"Governance
RealWtime"Analytics
NoSQL"Doc"Store Data"Warehouse Deep"Analytics,"
Modeling
Transactional"
Systems
Landing,"
Exploration,"
Archive
Reporting,"Analytics
Logical'Data'Warehouse

17. ©"2015"IBM"Corporation17 IBM"Confidential
On'Premise Cloud
Fluid'Query
IBM"Fluid"Query"– Powering"Big"Data"Analytics
" Intelligently"route"queries"to"the"
correct"data"store
" Simplify"and"unify"information"
access"for"end"users"and"
applications
" Access"all"data"within"the"
logical"data"warehouse"for"
analytics"and"business"insight
Move'the'query'to'the'data,'not'the'data'to'the'query
Question
Answer
Hadoop
Data'Warehouse
Data'Mart
Operational
Other
In#the#world#of#big#data,#can#you#really#afford#to#move#
all#your#data#to#the#analytics?

24. 1"©"Cloudera,"Inc."All"rights"reserved."
Enterprise"Data"Warehouse"
Op=miza=on"
TJ"Laher"|"Product"Marke=ng,"Business"Solu=ons"

25. 2"©"Cloudera,"Inc."All"rights"reserved."
Trends"in"the"Market"
16#billion"connected"devices"
genera=ng"more"data"
"
“It"will"soon"be"technically#
feasible#&#affordable#to"record"
&"store"everything…”"
ELT"drives"up"to"80%"of"
database"capacity"
"
Internet"of"Things" Data"Storage"Costs" Resource"Intensive"ELT"
Trends"Driving"Change"
Source:(Forbes( Source:(New(York(Times( Source:(Syncsort((

26. 3"©"Cloudera,"Inc."All"rights"reserved."
Challenges"with"a"Tradi=onal"Architecture"
1) Limited#Data#Ingest#
Enterprise"Data"Warehouse"
Applica@ons#Data#Sources#
Structured"
Unstructured"
Ingest"
Staging#Environment##
Tradi@onal#Architecture##
Enterprise#Data#Warehouse#
Serve"ELT"
Archive"
BI"System"
Modeling"
Repor=ng"
ETL"
Storage"#1"
Storage"#2"
Storage"N"
Ingest"
Process"
Load"
1"

27. 4"©"Cloudera,"Inc."All"rights"reserved."
Challenges"with"a"Tradi=onal"Architecture"
1) Limited#Data#Ingest# 2)#Inefficient#Data#Processing#
#
Enterprise"Data"Warehouse"
Applica@ons#Data#Sources#
Structured"
Unstructured"
Ingest"
Staging#Environment##
Tradi@onal#Architecture##
Enterprise#Data#Warehouse#
Serve"ELT"
Archive"
BI"System"
Modeling"
Repor=ng"
ETL"
Storage"#1"
Storage"#2"
Storage"N"
Ingest"
Process"
Load"
1"
2"
2"

28. 5"©"Cloudera,"Inc."All"rights"reserved."
Challenges"with"a"Tradi=onal"Architecture"
1) Limited#Data#Ingest# 2)#Inefficient#Data#Processing#
#
3)#Data#Archived#
#
Enterprise"Data"Warehouse"
Applica@ons#Data#Sources#
Structured"
Unstructured"
Ingest"
Staging#Environment##
Tradi@onal#Architecture##
Enterprise#Data#Warehouse#
Serve"ELT"
Archive"
BI"System"
Modeling"
Repor=ng"
ETL"
Storage"#1"
Storage"#2"
Storage"N"
Ingest"
Process"
Load"
1"
2"
2"
3"

29. 6"©"Cloudera,"Inc."All"rights"reserved."
A"New"Way"Forward"
1)#Ingest#More#Data#
Applica@ons#Data#Sources#
Structured"
Unstructured"
Staging#Environment##
Modern#Architecture##
Enterprise#Data#Warehouse#
EDH"Ingest"
Ac=ve"
Structured"Data"
Serve"
Serve"
ELT"
Archive"
Load"
1"
ETL"
BI"System"
Modeling"
Repor=ng"

30. 7"©"Cloudera,"Inc."All"rights"reserved."
A"New"Way"Forward"
1)#Ingest#More#Data# 2)#Op@mize#Data#Processing#
Applica@ons#Data#Sources#
Structured"
Unstructured"
Staging#Environment##
Modern#Architecture##
Enterprise#Data#Warehouse#
EDH"Ingest"
Ac=ve"
Structured"Data"
Serve"
Serve"
ELT"
Archive"
Load"
2"
1"
ETL"
BI"System"
Modeling"
Repor=ng"

31. 8"©"Cloudera,"Inc."All"rights"reserved."
A"New"Way"Forward"
1)#Ingest#More#Data# 2)#Op@mize#Data#Processing# 3)#Automated#Secure#Archive#
Applica@ons#Data#Sources#
Structured"
Unstructured"
Staging#Environment##
Modern#Architecture##
Enterprise#Data#Warehouse#
EDH"Ingest"
Ac=ve"
Structured"Data"
Serve"
Serve"
ELT"
Archive"
Load"
2"
3"1"
ETL"
BI"System"
Modeling"
Repor=ng"

32. 9"©"Cloudera,"Inc."All"rights"reserved."
Thank"you."

33. Questions?

34. THANK
YOU!
FIND OUT MORE at
http://insideanalysis.com/research/modern-data-warehouse