CTO Snehal’Antani’...Dev/Test Cloud DevPaern’ QAProd’PaYern’ Production Cloud...
Transcript of CTO Snehal’Antani’...Dev/Test Cloud DevPaern’ QAProd’PaYern’ Production Cloud...
Copyright © 2015 Splunk Inc.
Snehal Antani CTO, Splunk
• CTO
Commercial Intensity Market Speed
Earning a seat at the table
We should be running the
business in real >me!
Why am I making decisions on
last month’s data?
Data from the past RealCme Data StaCsCcal Forecast T – a few days T + a few days
Security OperaCons Center
IT OperaCons Center
Business OperaCons Center
DescripCve (BI Tools, Data Lakes) Grey space PredicCve
(Models)
Security Opera>ons
IT Opera>ons
Business Opera>ons
SAME DATA Of the
Asking different QUESTIONS
Different PEOPLE
Fast-‐feedback loop for acConable commercial insights
Responsibly Move at Market Speed
PRODUCT MANAGERS DEVELOPERS
IT BUSINESS
APPLICATION PLATFORM
HOW IS OUR: • Security? • Quality? • Stability? • Performance?
HOW IS OUR: • Market Launch? • Feature Usage? • MarkeCng Changes?
• PrioriCzaCon?
IoT WORKLOADS
Blurring the Lines Between Digital & Physical
Security Ops
Center
Business Ops Center
IT Ops Center
CLOUD WORKLOADS ENTERPRISE IT WORKLOADS
ADVANCED CORRELATION HYBRID DEPLOYMENT
SINGLE PANE OF GLASS
2. Focus on the foundaCon
3. Divide, empower, and deliver
4. Ignore the “Eeyores”
1. Define your outcomes
• Development Velocity • Failed Customer Interactions • Compliance Response Time
App Teams
App PlaWorm
Hybrid Cloud
Con>nuous Delivery
Con>nuous Insights
But how do you get there?
Install App Servers
Install OS’
Install Physical Servers
Configure network
Configure security
Debug! Servers Storage Networking
Virtualization
Management
Security
Middleware Patterns
Automated Deployment
Applications
1. Hybrid Cloud Fewer “moving parts” for speed & stability
Get out of the “IT integraCon business”
Challenge: People & processes, not tech
Dev/Test Cloud
Dev PaYern QA Prod PaYern
Production Cloud
QA Prod PaYern
Enterprise Architecture
PaYern Library Developer Dev, QA, & Prod provisioned in minutes
Speed, Consistency, & Repeatability with Pa`erns
2. ConCnuous Delivery
Sonar code quality Sta>c Security Scans
Build cloud Developer checks in code
Automated Acceptance Tests
Dynamic Security Scans
Test Cloud
“Chaos Monkey” tests
Automated Deploy
=
=
X
X
Production Cloud
QA Prod PaYern
Automated Deploy
PaYern Library
Real-‐Cme Dashboards Ensure Transparency
• Best / worst developers • Custom training programs • Best / worst outsourced providers
• Visualize • Analyze • Take Ac>on
… quickly … automaCcally
3. ConCnuous Insights: ProacCve, not reacCve
QA Prod PaYern
How?
Web CoE Middleware CoE DB CoE
CoE Specific KPI’s & Dashboards
Business TransacCon KPI’s & Dashboards
“Canary” Thumbs up / down
“Band-‐aids” Document – Script – Trust – Auto-‐execute
“War Room” CollaboraCve TroubleshooCng
& Post-‐mortem
IT Opera>ons Center
Detect PotenCal Failed Customer InteracGons…
… and do something about it!
OperaConal Intelligence Plaiorm
Web Logs
App Logs
Network Logs
Endpoint Logs
IdenCty Logs
Wearables Vehicles
…
Industrial Control
SCADA
…
IT DATA
IOT DATA
OT DATA
Time
HOT WARM
Data Aging Policy
COOL COLD
Historical Near Real-‐Cme PredicCve
High-‐speed Data Ingest
T0
PREDICT ALERT SEARCH VISUALIZE ANALYZE DEVELOP Enrich ACT
InvesGgate the data in real-‐Cme
• Define StaCc schema • ETL into Schema • Enrich at write • New data = new columns • New quesCons = new columns
Ideal for asking the same quesGon repeatedly
• Schema-‐on-‐the-‐fly • Data in naCve format • Enrich on read • New data = no changes needed • New quesCons = no changes needed
Ideal for invesGgaGon & exploraGon
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Schema-‐on-‐write Schema-‐on-‐read
Establish a culture of ConGnuous Improvement
Pivot & improve with Con>nuous Insights
Product Managers idenCfy new opportuniCes
Con>nuously delivered to market
… and Auditors are “happy”
Fast-‐feedback enables Market Speed
With a Strong IT foundaCon, and the right culture…
Commercial Intensity + Market Speed =
Resources of an Enterprise, Agility of a startup
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Data Science Development Life Cycle
IT DATA
IOT DATA
OT DATA
Time T0
Analyze & Develop models Develop
Back test
Deploy models at the edge
HOT WARM COOL COLD
Historical Near Real-‐Cme PredicCve
Data Lake