In Chip Biz Analytics
Innovation & Disruption
Amir Orad
Sisense CEO
July 2016
Quick Background
Inventor/co-founder of Cyber Analytics company Cyota (RSA)
CEO of $200M Financial Crime analytics company Actimize (NICE)
Leading Sisense to Simplify Complex Data Analytics
Columbia University MBA
What Do Five Data Geek
Students Dream About
BEERS & CHIPS
Technology Disruption
Traditional BI
1995
In-Memory BI
2005
In-Chip BI
2015
Database Camp 2016 @ United Nations, NYC - Amir Orad, CEO, Sisense
Memory Bandwidth – Data Size vs Speed
Too SlowX50
X10
L1 cache L2 / L3 cache RAM DiskDistance from L1 = slowdown
Data Beer
IN ORDER TO
UNDERSTAND
IN-CHIP
ANALYTICS
LET’S ASSUME THAT:
Memory bandwidth 2
L1 cache Home fridge Distance Immediate Customer
x1
L2 / l3 cache Shop Distance Bicycle Customer
x10
Ram
Supermarket Distance Car Customer
x50
Disk Brewery Distance Airplane Customer
If data equals beer then data storage units equals
Vectorization & Cache Awareness
L1Cache
FirstintoRAM
OP
100
4K
(Values) 100
4K
(Values)
100
4K
(Values)
Result Vector
Push Back To RAM
100
4K
(Values)
SIMD REGISTER
Apply Operation On
4/8 Data Elements
Simultaneously
OP
OP
Column4
100
4K
(Values)
ResultVector
100
4K
(Values)
Column1
100
4K
(Values)
Column2
100
4K
(Values)
Column3
100
4K
(Values)
Our Technology In memory columnar
execution mode
CACHE aware query
kernel
CACHE aware
decompression
Instruction recycling &
learning algorithms
LLVM based compiler
with SIMD support
Full 64BIT support
Columnar storage
SPEED!
STRATA AWARD
Analyzing 10TB of data In 10 seconds
On a single node on a standard Dell Server
Empowering growth, anywhere everywhere, on affordable HW
300% improvement in efficiency and
speed with every new chip Intel
releases vs. 30% industry average
1
3
9
Intel Chip Generation
PerformanceIndex
Sisense
Industry
Are We Solving the Real Problem?
Breaking an Assembly Line Tradition
Database Camp 2016 @ United Nations, NYC - Amir Orad, CEO, Sisense
Need DBA to build database
Define what data will be queried
Join tables upfront
Normalize and create a star schema
Why?
Surprising Benefits
Handle complex data faster, cheaper, easier
Boost performance 10X-100X; Cut HW reqs
Eliminate & simplify data preparations
Save precious DBA/IT time
Eliminate manual Join, Index, Star Schema
Fast to deploy; Agile to change
Self service for everyone - Biz & IT
Shrink TCO & time to insight
Technology Disruption Results
DW, OLAP
Complex
“Expensive” mash-up
TB Scale
Months
Traditional BI
1995
In-Memory
Simple for Biz
Manual mash-up
GB Scale
Weeks
In-Memory BI
2005
In-Chip
Simpler for Biz & IT
Ad-hoc mash-up
TB Scale
Days
In-Chip BI
2015
Sisense Technology Disruption: Single StackTM and In-ChipTM
Single-StackTM
Replace 4 layers with 1 tool
Database, ETL
Analytics, Visualization
For Biz Users, no IT/DBA
NO hodgepodge of tools
In-ChipTM
Patent pending Proprietary tech
In-Memory  In-Chip analytics
NO need to:
Prepare data, schema
Define indexes, joins
Analytics Capabilities
PRESCRIPTIVE
How Can We Make it Happen?
PREDICTIVE
What Will Happen?
DIAGNOSTIC
Why Did it Happen?
DESCRIPTIVE
What Happened?
FINANCIALS GROWTH
Compare monthly
financials current to
previous year
ANOMALY DETECTION
Identify anomalies in
attacks resulting in
cyber incidents
MORTALITY RISK
Predict and segment
patients based on risk
of diabetes
RECOMMENDATION
Optimize sales by
recommending
products purchased
Examples
A Dream Comes True – 1000+ Clients
Lessons Learned
Dream BIG
Refine, refine, refine benefits
Don’t automate, obliterate!
Disrupt, don’t improve
Thank You  Demo
Free Trial
www.sisense.com

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Database Camp 2016 @ United Nations, NYC - Amir Orad, CEO, Sisense

  • 1. In Chip Biz Analytics Innovation & Disruption Amir Orad Sisense CEO July 2016
  • 2. Quick Background Inventor/co-founder of Cyber Analytics company Cyota (RSA) CEO of $200M Financial Crime analytics company Actimize (NICE) Leading Sisense to Simplify Complex Data Analytics Columbia University MBA
  • 3. What Do Five Data Geek Students Dream About
  • 7. Memory Bandwidth – Data Size vs Speed Too SlowX50 X10 L1 cache L2 / L3 cache RAM DiskDistance from L1 = slowdown
  • 8. Data Beer IN ORDER TO UNDERSTAND IN-CHIP ANALYTICS LET’S ASSUME THAT:
  • 9. Memory bandwidth 2 L1 cache Home fridge Distance Immediate Customer x1 L2 / l3 cache Shop Distance Bicycle Customer x10 Ram Supermarket Distance Car Customer x50 Disk Brewery Distance Airplane Customer If data equals beer then data storage units equals
  • 10. Vectorization & Cache Awareness L1Cache FirstintoRAM OP 100 4K (Values) 100 4K (Values) 100 4K (Values) Result Vector Push Back To RAM 100 4K (Values) SIMD REGISTER Apply Operation On 4/8 Data Elements Simultaneously OP OP Column4 100 4K (Values) ResultVector 100 4K (Values) Column1 100 4K (Values) Column2 100 4K (Values) Column3 100 4K (Values)
  • 11. Our Technology In memory columnar execution mode CACHE aware query kernel CACHE aware decompression Instruction recycling & learning algorithms LLVM based compiler with SIMD support Full 64BIT support Columnar storage
  • 12. SPEED! STRATA AWARD Analyzing 10TB of data In 10 seconds On a single node on a standard Dell Server
  • 13. Empowering growth, anywhere everywhere, on affordable HW 300% improvement in efficiency and speed with every new chip Intel releases vs. 30% industry average 1 3 9 Intel Chip Generation PerformanceIndex Sisense Industry
  • 14. Are We Solving the Real Problem?
  • 15. Breaking an Assembly Line Tradition
  • 17. Need DBA to build database Define what data will be queried Join tables upfront Normalize and create a star schema Why?
  • 18. Surprising Benefits Handle complex data faster, cheaper, easier Boost performance 10X-100X; Cut HW reqs Eliminate & simplify data preparations Save precious DBA/IT time Eliminate manual Join, Index, Star Schema Fast to deploy; Agile to change Self service for everyone - Biz & IT Shrink TCO & time to insight
  • 19. Technology Disruption Results DW, OLAP Complex “Expensive” mash-up TB Scale Months Traditional BI 1995 In-Memory Simple for Biz Manual mash-up GB Scale Weeks In-Memory BI 2005 In-Chip Simpler for Biz & IT Ad-hoc mash-up TB Scale Days In-Chip BI 2015
  • 20. Sisense Technology Disruption: Single StackTM and In-ChipTM Single-StackTM Replace 4 layers with 1 tool Database, ETL Analytics, Visualization For Biz Users, no IT/DBA NO hodgepodge of tools In-ChipTM Patent pending Proprietary tech In-Memory  In-Chip analytics NO need to: Prepare data, schema Define indexes, joins
  • 21. Analytics Capabilities PRESCRIPTIVE How Can We Make it Happen? PREDICTIVE What Will Happen? DIAGNOSTIC Why Did it Happen? DESCRIPTIVE What Happened? FINANCIALS GROWTH Compare monthly financials current to previous year ANOMALY DETECTION Identify anomalies in attacks resulting in cyber incidents MORTALITY RISK Predict and segment patients based on risk of diabetes RECOMMENDATION Optimize sales by recommending products purchased Examples
  • 22. A Dream Comes True – 1000+ Clients
  • 23. Lessons Learned Dream BIG Refine, refine, refine benefits Don’t automate, obliterate! Disrupt, don’t improve
  • 24. Thank You  Demo Free Trial www.sisense.com

Editor's Notes

  • #4: Beer and Data