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Accelerating DevOps with Virtual Data 
1 
http://kylehailey.com 
kyle@delphix.com 
Tim Gorman 
tim@delphix.com
Accelerating the tempo of application development 
Fortune 1 Retailer #1 Social Network #1 Financial Services #1 Network Equipment #1 Cable 
#1 Wholesale #1 Food Service Co. #1 Office Supplies #1 Apparel & Footwear #1 Chip Manufacturing 
#1 Pharma #1 Auction Marketplace #1 Total Healthcare #1 Aerospace #1 Computer Access 
#1 CPG #1 ETL Software #1 Availability Service #1 Mutual Life Ins. #1 Satellite TV 
#1 State Gov #1 Cruise Line #1 Retirement Fund #1 IT Services #1 Game Software 
© 2014 Delphix. All Rights Reserved Private and confidential 2
Are you too busy to Innovate? 
Inertia
What is DevOps = tools + culture 
• Culture : 
– Empathy 
– Collaboration 
– Bridging silos, avoid blame 
• Tools : 
– Automation 
– Measurement 
– Self-service 
4
Note: DevOps > Tools + Culture 
DevOps= optimizing flow from Dev to Ops to Pro 
5 
“Do not seek to follow in the footsteps of the wise. 
Seek what they sought” 
- Matsuo Bashō 
Goal = company’s bottom line
The Goal : Theory of Constraints 
Improvement 
not made 
at the constraint 
is an illusion 
factory floor optimization
Factory floor
Factory floor 
constraint 
Not a relay race
Tune before constraint 
constraint 
Tuning here 
Stock piling
Tune after constraint 
constraint 
Tuning here 
Starvation
Factory floor : straight forward 
constraint 
Goal: find constraint 
optimize it
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”
The Phoenix Project 
What is the 
constraint 
in IT ?
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
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
In this presentation : 
• Data Constraint 
• Solution 
• Use Cases
• Data Constraint 
• Solution 
• Use Cases
moving data is hard 
– Storage & Systems 
– Personnel 
– Time
Typical Architecture 
Production 
Instance 
Database 
File system
Typical Architecture 
Production 
Instance 
Backup 
Database 
File system 
Database 
File system
Typical Architecture 
Production 
Instance 
Reporting Backup 
Database 
File system 
Instance 
Database 
File system 
Database 
File system
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
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
Data floods infrastructure 
92% of the cost of business, 
in financial services business , is “data” 
www.wsta.org/resources/industry-articles 
Most companies average 
5% IT spending , ½ on “data” 
http://uclue.com/?xq=1133
Four Areas hit by data constraint 
1. IT Capital resources $ 
2. IT Operations personnel $ 
3. Application Development $$$ 
4. Business $$$$$$$
1. Hardware – copies take up space 
–Servers 
–Storage 
–Network 
–Data center floor space, power, cooling
$ Never enough environments
$ IT Operations – copying data takes people time 
• People 1000s hours per year just for DBAs 
– DBAs 
– SYS Admin 
– Storage Admin 
– Backup Admin 
– Network Admin 
• $100s Millions for data center modernizations
$ Application Development – wait for copies 
• 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
$ Business – decisions depend on data access 
Ability to capture revenue 
• Business Applications 
– Delays cause lost revenue 
• Business Intelligence 
– Old data = less intelligence
companies unaware
companies unaware 
Boss, Storage Admin, DBA Developer or Analyst
companies unaware 
Metrics 
– Time 
– Old Data 
– Storage 
Other 
– Analysts 
–Audits
What Problems does Data Constraint Cause 
1. Bottlenecks 
2. Waiting for environments 
3. Waiting to check in code 
4. Production Bugs 
5. Expensive Slow QA
Your Project 
Available 
Resources
Development : bottlenecks 
Frustration Waiting
Development : Bugs 
Old Unrepresentative Data
Development : subsets 
False Negatives 
False Positives 
Bugs in Production
Production Wall 
40
Development : silos
QA : Long Build times 
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)
DevOps and Data : Impossible? 
Life with Waterfall 
Dream of Agile & CI
Waterfall vs Agile 
44
Missed ! 
Goal 
Agile & CI vsWaterfall 
Agile & CI Achieved !
bugs 
time 
Missed ! 
Goal 
Agile & CI Achieved ! 
Bugs
profit 
time 
Missed ! 
Goal 
Agile & CI Achieved ! 
Profit
Missed ! 
Goal 
Cost per Deployment 
Agile & CI Achieved ! 
Cost 
Per 
Deployment time
DevOps and Data : Impossible? 
• Data & DevOps : Impossible ? 
• 20 copies of production a day for CI 
• Each copy is like
In this presentation : 
• Data Constraint 
• Solution 
• Use Cases
99% of blocks are identical 
Clone 1 Clone 2 Clone 3
Solution
Thin Clone 
Clone 1 Clone 2 Clone 3
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
Fuel not equal car 
Challenges 
1. Technical 
2. Bureaucracy
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)
1hour 
9 days 
1 day 
Why are hand offs so expensive? 
Bureaucracy
Technical Challenge 
Production Filer 
Database 
Luns 
Target A 
Target B 
Target C 
snapshot 
clones 
InsIntsatannccee 
InInssttaanncece 
InInssttaannccee 
InInssttaannccee 
Instance 
Source
Development Filer 
Production Filer clones 
Database 
LUNs 
snapshot 
Technical Challenge 
Instance 
Target A 
InInssttaannccee 
Target B 
InInssttaanncece 
Target C 
InInssttaannccee 
Instance
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
How to get a Data Virtualization? 
2 1 
– EMC + SRDF 
– Netapp 2 + SMO 
1 
– Oracle EM 12c + data guard + Netapp /ZFS 
– Actifio - hardware 
– Delphix - software 
3 1 2 
Source 
sync 
Deploy 
automation 
Storage 
snapshots 
1 2 3
Goal : virtualize, govern, deliver 
62 
• Masking: Masking 
• Security: Chain of custody 
• Self Service: Logins 
• Developer: Versioning , branching 
• Audit: Live Archive 
Data Supply Chain 
Data Virtualization 
Thin Cloning 
Snap Shots
Dev 
Production Time Flow 
Prod 
2.6 
Dev finishes a sprint or point 
release and QA forks off a clone 
virtual database from Dev 
database
Dev 
QA 
Production Time Flow 
Prod 
2.6 
Continuous integration 
Nightly or hourly regressions
Dev 
QA 
Production Time Flow 
Prod 
2.6 
Dev finishes a sprint or point 
release and QA forks off a clone 
virtual database from Dev 
database
Dev 
QA 
Production Time Flow 
Prod 
2.6 
Dev finishes a sprint or point 
release and QA forks off a clone 
virtual database from Dev 
database 
UAT
Prod 
Dev 
2.7 
QA 
UAT 
Production Time Flow 
UAT 
QA 
Dev 
2.6
Intel hardware 
DB2 
Data 
File Systems 
Binaries 
Install Delphix on x86 hardware
Allocate Any Storage to Delphix 
Allocate Storage 
Any type 
Pure Storage + Delphix 
Better Performance for 
1/10 the cost
One time backup of source database 
Production 
InsIIntnsasttanannccceee 
Database 
File system
DxFS (Delphix) Compress Data 
Production 
InsIIntnsasttanannccceee 
Database 
Data is 
compressed 
typically 1/3 
size 
File system
Incremental forever change collection 
Production 
Database 
File system 
Changes 
Time Window 
• Collected incrementally forever 
• Old data purged 
InsIIntnsasttanannccceee
Snapshot 1 – full backup once only at link time 
Jonathan Lewis © 2013 
Virtual DB 
73 / 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.
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
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
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
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)
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’
Database Virtualization
Three Physical Copies 
Three Virtual Copies 
Data 
Virtualization 
Appliance
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”
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
• Problem in the Industry 
• Solution 
• Use Cases
Use Cases 
1. Development and QA 
2. Production Support 
3. Business
Use Cases 
1. Development and QA 
2. Production Support 
3. Business
Development: Virtual Data 
• Unlimited 
• Full size 
• Self Service 
Development
Virtual Data: Easy 
Instance 
Instance 
Instance 
Instance 
Source 
DVA
Development Virtual Data: Parallelize 
gif by Steve Karam
Development Virtual Data: Full size
Development Virtual Data: Self Service
QA : Virtual Data 
• Fast 
• Parallel 
• Rollback 
• A/B testing
Dev 
QA 
QA Virtual Data : Fast 
Prod 
Instance 
DVA 
• Low Resource 
• Find bugs Fast 
Production Time Flow
QA with Virtual Data: Rewind 
Instance 
QA 
Prod 
Production Time Flow
QA with Virtual Data: A/B 
Instance 
Instance 
Instance 
Index 1 
Index 2 
Production Time Flow
Data Version Control 
Dev 
QA 
2.1 
Dev 
QA 
2.2 
DVA Production Time Flow 
2.1 2.2 
Prod 
Instance 
11/11/2014 95
Use Cases 
1. Development and QA 
2. Production Support 
3. Business
• Backups 
• Recovery 
• Forensics 
• Migration 
• Consolidation 
Recovery
9TB database 1TB change day 
30 day backups storage requirements 
98 
70 
60 
50 
40 
30 
20 
10 
0 
week 1 
week 2 
week 3 
week 4 
original 
Oracle 
Delphix
Recovery 
Source 
Instance 
Recover VDB 
Instance 
Drop 
DVA Production Time Flow
Forensics 
Instance 
Development 
DVA 
Source 
Production Time Flow
Development (the new production) 
Instance 
Development 
DVA 
Source 
Development 
Prod & VDB Time Flow 
X
Migration
Consolidation
Use Cases 
1. Development and QA 
2. Production Support 
3. Business Intelligence
Business Intelligence 
• ETL 
• Temporal 
• Confidence Testing 
• Federated Databases 
• Audits
Business Intelligence: ETL and Refresh Windows 
1pm 10pm 8am 
noon
Business Intelligence: batch taking too long 
1pm 10pm 8am 
noon 
2011 
2012 
2013 
2014 
2015
6am 8am 10pm 
10pm 8am noon 9pm 
1pm 10pm 8am 
noon 
2011 
2012 
2013 
2014 
2015
Business Intelligence: ETL and DW Refreshes 
Prod 
Instance 
DW & BI 
Instance
Virtual Data: Fast Refreshes 
• Collect only Changes 
• Refresh in minutes 
Prod 
Instance 
BI and DW 
ETL 
24x7 
DVA 
Production Time Flow
Temporal Data
Confidence testing
Modernization: Federated 
Source1 
Instance 
Source2 
Instance 
DVA 
Production Time Flow 1 
Production Time Flow 2
Modernization: Federated
Modernization: Federated 
“I looked like a hero” 
Tony Young, CIO Informatica
Live Archive 
Production Time Flow 
Audit 
Prod 
Instance 
DVA 
11/11/2014 116
Use Case Summary 
1. Development & QA 
2. Production Support 
3. Business
How expensive is the Data Constraint? 
DVA at Fortune 500 : 
Dev throughput increase by 2x
How expensive is the Data Constraint? 
Faster 
• Financial Close 
• BI refreshes 
• Surgical recovery 
• Projects
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
Summary 
• Problem: Data is the constraint 
• Solution: Virtualize Data 
• Results: 
• Half the time for projects 
• Higher quality 
• Increase revenue
Thank you! 
• Kyle Hailey| Oracle ACE and Technical 
Evangelist, Delphix 
– Kyle@delphix.com 
– kylehailey.com 
– slideshare.net/khailey
124

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Denver devops : enabling DevOps with data virtualization

  • 1. Accelerating DevOps with Virtual Data 1 http://kylehailey.com kyle@delphix.com Tim Gorman tim@delphix.com
  • 2. Accelerating the tempo of application development Fortune 1 Retailer #1 Social Network #1 Financial Services #1 Network Equipment #1 Cable #1 Wholesale #1 Food Service Co. #1 Office Supplies #1 Apparel & Footwear #1 Chip Manufacturing #1 Pharma #1 Auction Marketplace #1 Total Healthcare #1 Aerospace #1 Computer Access #1 CPG #1 ETL Software #1 Availability Service #1 Mutual Life Ins. #1 Satellite TV #1 State Gov #1 Cruise Line #1 Retirement Fund #1 IT Services #1 Game Software © 2014 Delphix. All Rights Reserved Private and confidential 2
  • 3. Are you too busy to Innovate? Inertia
  • 4. What is DevOps = tools + culture • Culture : – Empathy – Collaboration – Bridging silos, avoid blame • Tools : – Automation – Measurement – Self-service 4
  • 5. Note: DevOps > Tools + Culture DevOps= optimizing flow from Dev to Ops to Pro 5 “Do not seek to follow in the footsteps of the wise. Seek what they sought” - Matsuo Bashō Goal = company’s bottom line
  • 6. The Goal : Theory of Constraints Improvement not made at the constraint is an illusion factory floor optimization
  • 8. Factory floor constraint Not a relay race
  • 9. Tune before constraint constraint Tuning here Stock piling
  • 10. Tune after constraint constraint Tuning here Starvation
  • 11. Factory floor : straight forward constraint Goal: find constraint optimize it
  • 12. 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”
  • 13. The Phoenix Project What is the constraint in IT ?
  • 14. 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
  • 15. 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
  • 16. In this presentation : • Data Constraint • Solution • Use Cases
  • 17. • Data Constraint • Solution • Use Cases
  • 18. moving data is hard – Storage & Systems – Personnel – Time
  • 19. Typical Architecture Production Instance Database File system
  • 20. Typical Architecture Production Instance Backup Database File system Database File system
  • 21. Typical Architecture Production Instance Reporting Backup Database File system Instance Database File system Database File system
  • 22. 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
  • 23. 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
  • 24. Data floods infrastructure 92% of the cost of business, in financial services business , is “data” www.wsta.org/resources/industry-articles Most companies average 5% IT spending , ½ on “data” http://uclue.com/?xq=1133
  • 25. Four Areas hit by data constraint 1. IT Capital resources $ 2. IT Operations personnel $ 3. Application Development $$$ 4. Business $$$$$$$
  • 26. 1. Hardware – copies take up space –Servers –Storage –Network –Data center floor space, power, cooling
  • 27. $ Never enough environments
  • 28. $ IT Operations – copying data takes people time • People 1000s hours per year just for DBAs – DBAs – SYS Admin – Storage Admin – Backup Admin – Network Admin • $100s Millions for data center modernizations
  • 29. $ Application Development – wait for copies • 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
  • 30. $ Business – decisions depend on data access Ability to capture revenue • Business Applications – Delays cause lost revenue • Business Intelligence – Old data = less intelligence
  • 32. companies unaware Boss, Storage Admin, DBA Developer or Analyst
  • 33. companies unaware Metrics – Time – Old Data – Storage Other – Analysts –Audits
  • 34. What Problems does Data Constraint Cause 1. Bottlenecks 2. Waiting for environments 3. Waiting to check in code 4. Production Bugs 5. Expensive Slow QA
  • 36.
  • 37. Development : bottlenecks Frustration Waiting
  • 38. Development : Bugs Old Unrepresentative Data
  • 39. Development : subsets False Negatives False Positives Bugs in Production
  • 42. QA : Long Build times 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)
  • 43. DevOps and Data : Impossible? Life with Waterfall Dream of Agile & CI
  • 45. Missed ! Goal Agile & CI vsWaterfall Agile & CI Achieved !
  • 46. bugs time Missed ! Goal Agile & CI Achieved ! Bugs
  • 47. profit time Missed ! Goal Agile & CI Achieved ! Profit
  • 48. Missed ! Goal Cost per Deployment Agile & CI Achieved ! Cost Per Deployment time
  • 49. DevOps and Data : Impossible? • Data & DevOps : Impossible ? • 20 copies of production a day for CI • Each copy is like
  • 50. In this presentation : • Data Constraint • Solution • Use Cases
  • 51. 99% of blocks are identical Clone 1 Clone 2 Clone 3
  • 53. Thin Clone Clone 1 Clone 2 Clone 3
  • 54. 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
  • 55. Fuel not equal car Challenges 1. Technical 2. Bureaucracy
  • 56. 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)
  • 57. 1hour 9 days 1 day Why are hand offs so expensive? Bureaucracy
  • 58. Technical Challenge Production Filer Database Luns Target A Target B Target C snapshot clones InsIntsatannccee InInssttaanncece InInssttaannccee InInssttaannccee Instance Source
  • 59. Development Filer Production Filer clones Database LUNs snapshot Technical Challenge Instance Target A InInssttaannccee Target B InInssttaanncece Target C InInssttaannccee Instance
  • 60. 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
  • 61. How to get a Data Virtualization? 2 1 – EMC + SRDF – Netapp 2 + SMO 1 – Oracle EM 12c + data guard + Netapp /ZFS – Actifio - hardware – Delphix - software 3 1 2 Source sync Deploy automation Storage snapshots 1 2 3
  • 62. Goal : virtualize, govern, deliver 62 • Masking: Masking • Security: Chain of custody • Self Service: Logins • Developer: Versioning , branching • Audit: Live Archive Data Supply Chain Data Virtualization Thin Cloning Snap Shots
  • 63. Dev Production Time Flow Prod 2.6 Dev finishes a sprint or point release and QA forks off a clone virtual database from Dev database
  • 64. Dev QA Production Time Flow Prod 2.6 Continuous integration Nightly or hourly regressions
  • 65. Dev QA Production Time Flow Prod 2.6 Dev finishes a sprint or point release and QA forks off a clone virtual database from Dev database
  • 66. Dev QA Production Time Flow Prod 2.6 Dev finishes a sprint or point release and QA forks off a clone virtual database from Dev database UAT
  • 67. Prod Dev 2.7 QA UAT Production Time Flow UAT QA Dev 2.6
  • 68. Intel hardware DB2 Data File Systems Binaries Install Delphix on x86 hardware
  • 69. Allocate Any Storage to Delphix Allocate Storage Any type Pure Storage + Delphix Better Performance for 1/10 the cost
  • 70. One time backup of source database Production InsIIntnsasttanannccceee Database File system
  • 71. DxFS (Delphix) Compress Data Production InsIIntnsasttanannccceee Database Data is compressed typically 1/3 size File system
  • 72. Incremental forever change collection Production Database File system Changes Time Window • Collected incrementally forever • Old data purged InsIIntnsasttanannccceee
  • 73. Snapshot 1 – full backup once only at link time Jonathan Lewis © 2013 Virtual DB 73 / 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.
  • 74. 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
  • 75. 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
  • 76. 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
  • 77. 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)
  • 78. 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’
  • 80. Three Physical Copies Three Virtual Copies Data Virtualization Appliance
  • 81. 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”
  • 82. 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
  • 83. • Problem in the Industry • Solution • Use Cases
  • 84. Use Cases 1. Development and QA 2. Production Support 3. Business
  • 85. Use Cases 1. Development and QA 2. Production Support 3. Business
  • 86. Development: Virtual Data • Unlimited • Full size • Self Service Development
  • 87. Virtual Data: Easy Instance Instance Instance Instance Source DVA
  • 88. Development Virtual Data: Parallelize gif by Steve Karam
  • 91. QA : Virtual Data • Fast • Parallel • Rollback • A/B testing
  • 92. Dev QA QA Virtual Data : Fast Prod Instance DVA • Low Resource • Find bugs Fast Production Time Flow
  • 93. QA with Virtual Data: Rewind Instance QA Prod Production Time Flow
  • 94. QA with Virtual Data: A/B Instance Instance Instance Index 1 Index 2 Production Time Flow
  • 95. Data Version Control Dev QA 2.1 Dev QA 2.2 DVA Production Time Flow 2.1 2.2 Prod Instance 11/11/2014 95
  • 96. Use Cases 1. Development and QA 2. Production Support 3. Business
  • 97. • Backups • Recovery • Forensics • Migration • Consolidation Recovery
  • 98. 9TB database 1TB change day 30 day backups storage requirements 98 70 60 50 40 30 20 10 0 week 1 week 2 week 3 week 4 original Oracle Delphix
  • 99. Recovery Source Instance Recover VDB Instance Drop DVA Production Time Flow
  • 100. Forensics Instance Development DVA Source Production Time Flow
  • 101. Development (the new production) Instance Development DVA Source Development Prod & VDB Time Flow X
  • 104. Use Cases 1. Development and QA 2. Production Support 3. Business Intelligence
  • 105. Business Intelligence • ETL • Temporal • Confidence Testing • Federated Databases • Audits
  • 106. Business Intelligence: ETL and Refresh Windows 1pm 10pm 8am noon
  • 107. Business Intelligence: batch taking too long 1pm 10pm 8am noon 2011 2012 2013 2014 2015
  • 108. 6am 8am 10pm 10pm 8am noon 9pm 1pm 10pm 8am noon 2011 2012 2013 2014 2015
  • 109. Business Intelligence: ETL and DW Refreshes Prod Instance DW & BI Instance
  • 110. Virtual Data: Fast Refreshes • Collect only Changes • Refresh in minutes Prod Instance BI and DW ETL 24x7 DVA Production Time Flow
  • 113. Modernization: Federated Source1 Instance Source2 Instance DVA Production Time Flow 1 Production Time Flow 2
  • 115. Modernization: Federated “I looked like a hero” Tony Young, CIO Informatica
  • 116. Live Archive Production Time Flow Audit Prod Instance DVA 11/11/2014 116
  • 117. Use Case Summary 1. Development & QA 2. Production Support 3. Business
  • 118. How expensive is the Data Constraint? DVA at Fortune 500 : Dev throughput increase by 2x
  • 119. How expensive is the Data Constraint? Faster • Financial Close • BI refreshes • Surgical recovery • Projects
  • 120. 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
  • 121.
  • 122. Summary • Problem: Data is the constraint • Solution: Virtualize Data • Results: • Half the time for projects • Higher quality • Increase revenue
  • 123. Thank you! • Kyle Hailey| Oracle ACE and Technical Evangelist, Delphix – Kyle@delphix.com – kylehailey.com – slideshare.net/khailey
  • 124. 124