This document discusses using a virtual data platform to address data constraints in IT. It begins by explaining how data flooding infrastructure strains IT resources and costs companies huge sums. Most companies are unaware of these data costs. The solution presented is a virtual data appliance that can clone database environments from snapshots to provision dev/test environments quickly without consuming large amounts of physical storage. Key benefits outlined include unlimited, full-sized, self-service database environments for development and QA teams as well as fast rollback capabilities and A/B testing for QA.

1. Virtual Data Platform:
or
Revolutionizing Database Cloning
How can the DBA make the biggest
impact on the company
1
http://kylehailey.com
kyle@delphix.com

2. The Goal : Theory of Constraints
Improvement
not made
at the constraint
is an illusion
factory floor optimization

3. Factory floor

4. Factory floor
constraint
Not a relay race

5. Tune before constraint
constraint
Tuning here
Stock piling

6. Tune after constraint
constraint
Tuning here
Starvation

7. Factory floor : straight forward
constraint
Goal: find constraint
optimize it

8. Theory of Constraints work for IT ?
• Goals Clarify
• Metrics Define
• Constraints Identify
• Priorities Set
• Iterations Fast
• CI
• Cloud
• Agile
• Kanban
• Kata
“IT is the factory floor of this century”

9. The Phoenix Project
What is the
constraint
in IT ?

10. What are the top 5 constraints in IT?
1. Dev environments setup
2. QA setup
3. Code Architecture
4. Development
5. Product management
“One of the most powerful
things that organizations
can do is to enable development
and testing to get
environment they need when
they need it“
- Gene Kim

11. Data is the constraint
CIO Magazine Survey:
60% Projects Over Schedule
85% delayed waiting for data
Data is the Constraint
only getting worse
Gartner: Data Doomsday, by 2017 1/3rd IT in crisis

12. In this presentation :
• Data Constraint
• Solution
• Use Cases

13. • Data Constraint
I. strains IT
II. price is huge
III. companies unaware
• Solution
• Use Cases

14. moving data is hard
– Storage & Systems
– Personnel
– Time

15. Typical Architecture
Production
Instance
Database
File system

16. Typical Architecture
Production
Instance
Backup
Database
File system
Database
File system

17. Typical Architecture
Production
Instance
Reporting Backup
Database
File system
Instance
Database
File system
Database
File system

18. Typical Architecture
Production
Instance
Database
File system
Triple Tax
Dev, QA, UAT Reporting Backup
Instance
Instance
Instance
Instance
Database
Database
File system
Database
File system
File system
Database
File system
Database
File system

19. Typical Architecture
Production
Instance
Database
File system
Instance
Instance
Instance
Instance
Database
Database
File system
Database
File system
File system
Database
File system
Database
File system

20. Data floods infrastructure
92% of the cost of business,
in financial services business , is “data”
www.wsta.org/resources/industry-articles
Most companies have
2-9% IT spending , ½ on “data”
http://uclue.com/?xq=1133

21. In this presentation :
• Data Constraint
I. strains IT
II. price is huge
III. companies unaware
• Solution
• Use Cases

22. price is Huge
Four Areas data tax hits
– IT Capital resources $
– IT Operations personnel $
– Application Development $$$
– Business $$$$$$$

23. $Hardware
–Servers
–Storage
–Network
–Data center floor space, power, cooling

24. $ Never enough environments

25. $ IT Operations
• People
– DBAs
– SYS Admin
– Storage Admin
– Backup Admin
– Network Admin
• Hours : 1000s just for DBAs
• $100s Millions for data center modernizations

26. $ Application Development
• Inefficient QA: Higher costs of QA
• QA Delays : Greater re-work of code
• Sharing DB Environments : Bottlenecks
• Using DB Subsets: More bugs in Prod
• Slow Environment Builds: Delays

27. $ Business
Ability to capture revenue
• Business Applications
– Delays cause lost revenue
• Business Intelligence
– Old data = less intelligence

28. In this presentation :
• Data Constraint
I. strains IT
II. price is huge
III. companies unaware
• Solution
• Use Cases

29. companies unaware

30. companies unaware
Boss, Storage Admin, DBA Developer or Analyst

31. companies unaware
Metrics
–Time
–Old Data
–Storage
–Analysts
–Audits

32. In this presentation :
• Data Constraint
I. strains IT
II. price is huge
III. companies unaware
• Solution
• Use Cases

33. 99% of blocks are identical
Clone 1 Clone 2 Clone 3

34. Solution

35. Thin Clone
Clone 1 Clone 2 Clone 3

36. Technology Core : file system snapshots
• EMC
– 16 snapshots on Symmetrix
– Write performance impact
– No snapshots of snapshots
• Netapp
– 255 snapshots
• ZFS
– Compression
– Unlimited snapshots
– Snapshots of Snapshots
• DxFS
– “”
– Storage agnostic
– Shared cache in memory
Also check out new SSD storage such as:
Pure Storage, EMC XtremIO

37. Fuel not equal car
Challenges
1. Technical
2. Bureaucracy

38. Bureaucracy
Developer Asks for DB Get Access
Manager approves
DBA Request
system
Setup DB
System
Admin
Request
storage
Setup
machine
Storage
Admin
Allocate
storage
(take snapshot)

39. 1hour
9 days
1 day
Why are hand offs so expensive?
Bureaucracy

40. Technical Challenge
Production Filer
Database
Luns
Target A
Target B
Target C
snapshot
clones
InsIntsatannccee
InInssttaanncece
InInssttaannccee
InInssttaannccee
Instance
Source

41. Development Filer
Production Filer clones
Database
LUNs
snapshot
Technical Challenge
Instance
Target A
InInssttaannccee
Target B
InInssttaanncece
Target C
InInssttaannccee
Instance

42. Technical Challenge
Production
Copy
Time Flow
Purge
Storage Development
File System Instance
1 2 3
Clone (snapshot)
Compress
Share Cache
Provision
Mount, recover, rename
Self Service, Roles & Security
Instance

43. How to get a Data Virtualization?
2 1
– EMC + SRDF
– Netapp 2 + SMO
1
– Oracle EM 12c + data guard + Netapp /ZFS
– Delphix
3 1 2
Source
sync
Deploy
automation
Storage
snapshots
1 2 3

44. Goal : virtualize, govern, deliver
• Security
• Masking
• Chain of custody
• Self Service
• Roles
• Restrictions
• Developer
• Data Versioning
• Refresh, Rollback
• Audit:
Data Supply Chain
Data Virtualization
Thin Cloning
• Live Archive Snap Shots
10/3/2014 44

45. Intel hardware
Unstructured
Data
Install Delphix on x86 hardware

46. Allocate Any Storage to Delphix
Allocate Storage
Any type
Pure Storage + Delphix
Better Performance for
1/10 the cost

47. One time backup of source database
Production
InsIIntnsasttanannccceee
Database
File system

48. DxFS (Delphix) Compress Data
Production
InsIIntnsasttanannccceee
Database
Data is
compressed
typically 1/3
size
File system

49. Incremental forever change collection
Production
Database
File system
Changes
Time Window
• Collected incrementally forever
• Old data purged
InsIIntnsasttanannccceee

50. Snapshot 1 – full backup once only at link time
Jonathan Lewis © 2013
Virtual DB
50 / 30
a b c d e f g h i
We start with a full backup - analogous to a level 0 rman backup. Includes
the archived redo log files needed for recovery. Run in archivelog mode.

51. Snapshot 2 (from SCN)
a b c d e f g h i
b' c'
The "backup from SCN" is analogous to a level 1
incremental backup (which includes the relevant
archived redo logs). Sensible to enable BCT.
Jonathan Lewis © 2013
Delphix executes
standard rman scripts

52. Apply Snapshot 2
a b b' c c' d e f g h i
The Delphix appliance unpacks the rman backup and "overwrites" the
initial backup with the changed blocks - but DxFS makes new copies of
the blocks
Jonathan Lewis © 2013

53. Drop Snapshot 1
a b' c' d e f g h i
The call to rman leaves us with a new level 0 backup, waiting for recovery.
But we can pick the snapshot root block. We have EVERY level 0 backup
Jonathan Lewis © 2013

54. Creating a vDB
a b' c' d e f g h i
The first step in creating a vDB is to take a snapshot of the filesystem as at
the backup you want (then roll it forward)
Jonathan Lewis © 2013
My vDB
(filesystem)
Your vDB
(filesystem)

55. Creating a vDB
a b' c' d e f g h i
The first step in creating a vDB is to take a snapshot of the filesystem as at
the backup you want (then roll it forward)
Jonathan Lewis
© 2013
My vDB
(filesystem)
Your vDB
(filesystem)
b'' c'' ff ii i’

56. Database Virtualization

57. Three Physical Copies
Three Virtual Copies
Data
Virtualization
Appliance

58. Before Virtual Data
Production Dev, QA, UAT
Instance
Reporting Backup
Database
File system
Instance
Instance
Instance
Instance
Database
Database
File system
Database
File system
File system
Database
File system
Database
File system
“triple data
tax”

59. With Virtual Data
Production
Instance
Dev & QA
Instance
InInssttaannccee
InInssttaannccee
Database
Reporting
Instance
Database
Backup
Database
Instance Instance Instance
Database
Database
Database
File system
Data
Virtualization
Appliance

60. In this presentation :
• Problem in the Industry
• Solution
• Use Cases

61. 1. Development and QA
2. Production Support
3. Business
Use Cases

62. 1. Development and QA
2. Production Support
3. Business
Use Cases

63. Development : bottlenecks
Frustration Waiting
Old Unrepresentative Data

64. Development : subsets
False Negatives
False Positives
Bugs in Production

65. Development : bugs

66. Development : slow env build times
http://martinfowler.com/bliki/NoDBA.html

67. Development: Virtual Data
• Unlimited
• Full size
• Self Service
Development

68. Virtual Data: Easy
Instance
Instance
Instance
Instance
Source
DVA

69. Development Virtual Data: Parallelize
gif by Steve Karam

70. Development Virtual Data: Full size

71. Development Virtual Data: Self Service

72. QA : Virtual Data
• Fast
• Parallel
• Rollback
• A/B testing

73. QA : Long Build times
QA Build QA
96% of QA time was building environment
$.04/$1.00 actual testing vs. setup
QA Build QA
X Bug
70
60
50
40
30
20
10
0
1 2 3 4 5 6 7
Delay in Fixing the bug
Cost
To
Correct
Software Engineering Economics – Barry Boehm (1981)

74. Dev
QA
QA Virtual Data : Fast
Prod
Instance
DVA
Time Flow
• Low Resource
• Find bugs Fast

75. QA with Virtual Data: Rewind
Instance
Development
Instance
Prod

76. QA with Virtual Data: A/B
Instance
Instance
Instance
Index 1
Index 2

77. Data Version Control
Dev
QA
2.1
Dev
QA
2.2
2.1 2.2
Prod
Instance
DVA
10/3/2014 77

78. 1. Development and QA
2. Production Support
3. Business
Use Cases

79. • Backups
• Recovery
• Forensics
• Migration
• Consolidation
Recovery

80. Backups

81. Recovery
Source
Instance
Recover VDB
Instance
Drop
DVA

82. Forensics
Instance
DVA Development
Instance
Source

83. Development (the new production)
Instance
Development
Instance
DVA
Source
Development
Instance

84. Migration

85. Consolidation

86. 1. Development and QA
2. Production Support
3. Business Intelligence
Use Cases

87. Business Intelligence
• ETL
• Temporal
• Confidence Testing
• Federated Databases
• Audits

88. Business Intelligence: ETL and Refresh Windows
1pm 10pm 8am
noon

89. Business Intelligence: batch taking too long
1pm 10pm 8am
noon
2011
2012
2013
2014
2015

90. 6am 8am 10pm
10pm 8am noon 9pm
1pm 10pm 8am
noon
2011
2012
2013
2014
2015

91. Business Intelligence: ETL and DW Refreshes
Prod
Instance
DW & BI
Instance

92. • Collect only Changes
• Refresh in minutes
Prod BI and DW
DVA Instance
Instance Instance
ETL
24x7
Virtual Data: Fast Refreshes

93. Temporal Data

94. Confidence testing

95. Modernization: Federated
Source1
Instance
Instance
Instance
Source2
Instance
DVA

96. Modernization: Federated

97. Modernization: Federated
“I looked like a hero”
Tony Young, CIO Informatica

98. Audit
Prod
Instance
DVA
Live Archive
10/3/2014 98

99. Use Case Summary
1. Development & QA
2. Production Support
3. Business

100. How expensive is the Data Constraint?
DVA at Fortune 500 :
Dev throughput increase by 2x

101. How expensive is the Data Constraint?
• Faster Financial Close
• Faster BI refreshes
• Faster surgical recovery
• More Project tracks
• Faster Projects

102. Virtual Data Quotes
• Projects “12 months to 6 months.”
– New York Life
• Insurance product “about 50 days ... to about 23 days”
– Presbyterian Health
• “Can't imagine working without it”
– State of California

104. Summary
• Problem: Data is the constraint
• Solution: Virtualize Data
• Results:
• Half the time for projects
• Higher quality
• Increase revenue

105. Oaktable World & hands on labs
105
We are
here
Oaktable
World
Moscone
South

106. Thank you!
• Kyle Hailey| Oracle ACE and Technical
Evangelist, Delphix
– Kyle@delphix.com
– kylehailey.com
– slideshare.net/khailey

107. Oracle 12c

108. 80MB buffer cache ?

109. 200GB
Cache

110. 5000
Latency Tnxs / min
300
ms
1 5 10 20 30 60 100 200
with
1 5 10 20 30 60 100 200
Users

111. 8000
Latency Tnxs / min
600
ms
1 5 10 20 30 60 100 200
Users
1 5 10 20 30 60 100 200

112. Five 200GB database copies are
cached with :
$1,000,000
1TB cache on SAN
$6,000
200GB shared cache on Delphix

113. 10/3/2014 113

114. 10/3/2014 114

115. Business Intelligence
a) 24x7 Batches
b) Temporal queries
c) Confidence testing

116. Thin Cloning

119. SnapManager
Repository
Snap
Manager
Protection
Manager
Snap
Manager
Snap Drive
Flex Clone
Snap Mirror
RMAN
Repository
Production
Development
DBA
Storage
Admin
1
tr-3761.pdf
Netapp

120. 1 Netapp
NetApp Filer - Production NetApp Filer - Development
Database
Luns
Snap
mirror
Snapshot Manager
for Oracle
Flexclone
Repository
Database
Snap
Drive
Protection
Manage
Production
Development
Target A
InInssttaannccee
Target B
InInssttaanncece
Target C
InInssttaannccee
Instance

121. Where we want to be
Production
Instance
Instance Instance Instance Instance
Database
File system
Development
Instance
Database
QA
Instance
Database
UAT
Instance
Database
Snapshots

122. EM 12c: Snap Clone
Production Development
Flexclone Flexclone
Netapp
Snap Manager for Oracle

123. II. Data constraint price is Huge : 4. Business

124. II. Data constraint price is Huge : 4.
Business
0 5 10 15 20 25 30
Revenue
Dev
IT Ops
Storage
Billion $

125. III. Data Constraint companies unaware
#1 Biggest Enemy :
IT departments believe
– best processes
– greatest technology
– Just the way it is

126. Are you Innovative?

127. III. Data Constraint companies unaware
Don’t we already do that ?
Why do I need an iPhone ?
SQL scripts
Alter database begin backup
Back up datafiles
Redo
Archive
Alter database end backup
RMAN

129. Merge to dev1 Dev1
Dev2
Trunk
DB
VC
Fork
Fork
Fork
Fork
DBmaestro

130. Modernization
1. Federated
2. Migration
3. Auditing

131. What is the constraint in IT
If you can’t satisfy
the business demands
then your process is broken.