Demystifying In-Memory Technologies: Best Uses and Competitive Advantages for the Enterprise

Best Uses and Competitive Advantages for the Enterprise
DEMYSTIFYING IN-MEMORY TECHNOLOGY

• Introduction
• Current Challenges and Trends
• What is In-Memory?
• Use Cases
• Implications, Considerations, Predictions, Musings
• Senturus Recommendations
• Special Offers
• Additional Resources
• Q & A
Today’s Agenda
2Copyright 2015 Senturus, Inc. All Rights Reserved

Michael Weinhauer
Practice Area
Director/Solutions Architect
Senturus
Introduction: Today’s Presenters
John Peterson
CEO & Co-Founder
Senturus

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Resource Library
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Who we are
SENTURUS INTRODUCTION

Technology Depth + Business Acumen
Senturus: Business Architects for Business
Analytics
C-Level
Business
Acumen
Technical/To
ol Expertise
Deep Data
Experience
Project
Management
Rigor
Business
Intelligence
Enterprise
Planning
Predictive
Analytics

A Few of Our 800+ Clients

Demystifying In-Memory Technology
TODAY’S WEBINAR

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Are you currently using in-memory technology or do you
plan to?
• Currently Implementing
• Implementing in the next 6 months
• Implementing in the next 12 months
• Evaluating for possible implementation beyond 12
months
• Not planning to implement
POLL

Background and History
CURRENT CHALLENGES

Can the speed at which you deliver analytics best be
described as…GLACIAL?
Setting the Stage

Are the number and type
of data sources in your
organization best
characterized as…
Setting the Stage
EXPLODING???!?

Current Challenges – Data Explosion
Velocity
Volume Variety
Mobile
PoS Data
Transactions
Customer Data
Real-time Replication
Sensor Data
Social Media
Predictive Analytics
Planning & Simulation
Location-based Services
Digital Communication
Log Data
RFID
Clickstream
VELOCITY
Worldwide digital content will
double in 18 months, and
every 18 months
thereafter.
IDC
In 2005, mankind created 150
Exabytes of information. In
2011, 1,200 Exabytes will
be created
The Economist
VOLUME
VARIETY
80% of enterprise data
will be unstructured
spanning traditional and non
traditional sources
Gartner
Copyright 2015 Senturus, Inc. All Rights Reserved

Is the speed of your business processes best expressed
on…
Setting the Stage
an evolutionary scale?

Setting the Stage
Do changes and additions to
your EDW require…
Superhuman Effort?

Are the demands for
real-time information
in your enterprise…
Setting the Stage
CRUSHING?

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Time Value of Information – Part I
Intelligence
Value
Triggered Event
Data Integrated & Ready for Analysis
Information Delivered
Time
Action Taken
Value
Diminishes
Data
Latency
Analysis
Latency
Decision
Latency
Action Time
Increased
Value
Action
Time
Reduced Time
To Action
Source: Dr. Richard Hackathorn. Bolder Technologies Inc.

Have you dreamt of
brilliant new ideas that
would catapult your
company light years ahead
of the competition, but
can’t suggest them because
you’ll sound…
Setting the Stage
21
CRAZY??!?

When Will Computers Be As Smart As Humans?

The Crux of the Problem – I/O is SLOW
10ms for spinning physical disk I/O
.2ms for SSD (50x faster)
.0001ms for RAM
- 2000x faster than SSD
- 100,000x faster than physical disk

Server Technology State in 1995
CPU
Single Core
16 CPUs Max
LAN/WAN
10base-10, T1 16-bit Applications
Clustering
limited, 2-Node
HDD
Small, Slow
1TB Max
RAM
Small, Expensive
2GB Max

HDD
Faster
Some SSD
Currently Implemented Technology State
CPU
Quad Core
Hyper-Threaded
LAN/WAN
Gigabit Ethernet
Optical OC-48
32-bit Applications
Some 64-bit
Clustering
4-8 node
RAM
Larger, Still Relatively Small
64-128GB

Currently Available Technology State
CPU
16 sockets
Multi-Core, MPP
LAN/WAN
100 Gigabit Ethernet
Optical OC-96 64-bit Applications
Clustering
Infinite Nodes
HDD
High Capacity
SSD
RAM
12TB
NVRAM

Trends – Logarithmic Drops in Cost and Increase
in Capacity
Copyright 2015 Senturus, Inc. All Rights Reserved.
1
10
100
1000
10000
100000
1000000
1980 1985 1990 1995 2000 2005 2010 2015
Historic Network and Broadband Speeds
LAN Speed (Mb/Sec) WAN Speed (Kb/Sec)
0.01
0.1
1
10
100
1000
10000
100000
$1
$10
$100
$1,000
$10,000
$100,000
$1,000,000
$10,000,000
1980 1985 1990 1995 2000 2005 2010 2013 2014
RAM Cost and Capacity
$/GB Size (MB)
1
100
10000
1000000
$0.01
$1.00
$100.00
$10,000.00
$1,000,000.00
1980 1985 1990 1995 2000 2005 2010 2013 2014
Historic (Spinning) HDD Cost and
Capacity
$/GB Size (MB)
0.1
1
10
100
1000
10000
100000
1000000
$0.00
$0.01
$0.10
$1.00
$10.00
$100.00
$1,000.00
$10,000.00
197819821985198919952000200520102014
Historic CPU Cost and Capacity
$/MIPS MIPS

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• A set of technologies and applications that take
advantage of high RAM-capacity hardware, optimized
for in-memory performance
– “in-memory” for our purposes refers to DRAM
– some SSD or hybrid
What Do We Mean by “In-Memory”?

Traditional Architecture
CPU
8k data
page8k data
page8k data
page8k data
page
8k data
page8k data
page8k data
page8k data
page
8k data
page8k data
page8k data
page8k data
page
8k data
page8k data
page8k data
page8k data
page
8k data
page8k data
page8k data
page8k data
page
8k data
page8k data
page8k data
page8k data
page
8k
data
page
8k
data
page
8k
data
page
8k
data
page
• Pages must be swapped in and
out of RAM
• Only Row Storage
• Limited Compression
• No special CPU instructions
• Stored Procedures run on disk
• Materialized Indexes/Views
Swaps

• That might help, but it’s not JUST about hardware (or
RAM)
• The application must be written to take advantage of
all this new technology
• DRAM is VOLATILE
– Power goes out – data goes POOF!
– Must be reloaded which can take significant time and/or
incur a performance hit
So Why Don’t I Just Get a Box w/a Bunch of RAM?

Row vs Column Store

Logical View - Compression Example
“CA” exists in 2 rows
“CA” can be stored
numerically, saving space

Vector Processing - SIMD

Disk vs In-Memory Storage

In-Memory Architecture
Data • Memory-optimized structures
• Dictionary-enabled
• Columnar/Hybrid Storage
• Compression
• Vector/SIMD Processing
• Compiled Stored Procedures
• Virtualized Indexes, Views
Load Once!
CPU
Data1-5
Data6-10
Data11-15
Data16-20
Data21-25
I
Data
Data
Data
Data
V
SP Red 1
Yellow 2
Blue 3
Green 4Data
Data
Data
Data
Data

Basic Types of In-Memory
38Copyright 2015 Senturus, Inc. All Rights Reserved.
Dynamic
Cubes
OLAP Data Discovery
& Visualization
Traditional
RDBMS Extensions
Intelligent
Appliances

And Many, MANY More…

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What are your top use cases for in-memory?
• Business process improvement
• Analytics – reporting and analysis
• Predictive/Prescriptive Analytics
• Landscape Simplification
POLL

• Transactional/Operational
– Simplify and optimize business processes
– Enable new business processes
– Embed operational reporting into transaction processing
• Landscape Simplification
– Lower TCO – more work out of a single server
– Eliminate redundant data and hardware
– Less power, cooling, floor space, manpower, maintenance
– Easier H/W upgrades
– Less/simpler ETL
– Bring the engine(s) to the data
Use Case Themes

• Analytical
– Agile Analytics
– Real-time
– Sentiment Analysis
– Ads
– Re-pricing
– Complex Event Processing of Streaming Sensor Data
– Predictive/Prescriptive
– Fraud Detection
– Machine Learning
– Geospatial
– Text Analysis
– Image/Video
– Mobile and Social
Use Case Themes

Data
Marts/OLAP
Simplifying the Stack = Speed and Agility
Data Warehouse
Indexes
Applications
Copy
ETL
Calculation Engine
Analytics
Query Results
Query
Up to 1,000x Faster
No Optimizations Required
Faster BI
Copyright 2015 Senturus, Inc. All Rights Reserved 45
Operational Data Store
Transactional Data
Aggregates

Data Warehouse
Data Warehouse
Calculation Engine
Query Results
Query
Data Warehouse
Applications
Copy
ETL
Analytics
Columnar Storage
2-5+x Compression of Data
Transactional Data
46

Calculation Engine
Query Results
Query
Analytics
MPP, Scale-up and Out Systems
Analytic Appliance
Applications
Copy
ETL
47
Data Warehouse
Transactional Data

Real-Time Data
If transactional systems are in-memory, redundant
environments like ODS may no longer be necessary
Analytics Analytic Appliance
Applications
Copy
ETL
Transactional Data
48

Operations and Analytics Together
Use a single environment for
both analytics and applications
Analytics Analytic Appliance
Applications
Copy
Transactional Data
49

“Traditional” Demand Forecasting Process
Total elapsed time: 3+ days
Multiple manual steps, several batch jobs, downloads/uploads
Business impact: Lower forecast accuracy, promise date attainment, higher Inventory
BW Extract
Load actual
demand data
from ERP into
BW
MC62/MC8V
Generate CVCs and
calculate proportions
TSCUBE
Transfer data from
Cube to Planning
Area
SDP94
Consensus DP
Process Chain
Transfer data to
APO/BW
Batch Job
Batch Job
Batch Job
Batch Job
BW Extract
Extract data from
Planning Area
and load into BW
Cube
Query
Analyze demand
plan
Batch Job
SAPAPO/MC8G
Release final
forecast to ERP
Batch Job
Extract
Load actual sell
through from
Demand Signal
System
Batch Job

Demand Forecasting Using In-Memory
BW Extract
Load actual
demand data
from ERP into
BW
MC62/MC8V
Generate CVCs and
calculate proportions
TSCUBE
Transfer data from
Cube to Planning
Area
SDP94
Consensus DP
Process Chain
Transfer data to
APO/BW
Batch Job
Batch Job
Batch Job
Batch Job
BW Extract
Extract data from
Planning Area
and load into BW
Cube
Query
Analyze demand
plan
Batch Job
SAPAPO/MC8G
Release final
forecast to ERP
Batch Job
Alerts
Forecast consumption
alert trigger, master data
change, customer news,
macro economic eventOther demand
signals
ERP quotes, CRM
opportunities,
Distributor data, etc.
Benefits:
• View backlog in real-time
• Same day planning
• No ERP data replication – access operational data directly
• Alert/exception driven process
Extract to In-
Memory dB
Extract
Load actual sell
through from
Demand Signal
System
Batch Job

AutoZone, Home Depot, Lowe’s – Real-time product availability
MKI - sequences DNA from biopsies, delivers targeted treatment regimen in
20 minutes, down from 2-3 days
Maple Leaf Foods – Reports that took 15-18 minutes to run take 15-18
seconds (60x improvement)
Citi – avoids costly FOREX delays using in-memory – 100ms costs $1m
Google - .5 second delay in search results in 20% traffic drop = lost revenue
Avon Cycle – Complex product delivery process improved from 15-20
minutes to a “few seconds”
Success Stories

IMPLICATIONS, PREDICTIONS,
CONSIDERATIONS, MUSINGS

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Copyright 2015 Senturus, Inc. All Rights Reserved. 55
Time Value of Information – Part II
Value Decreases Rapidly Over Time…
Data Acquisition
…until you need it again!
Archive Access Event
• Regulatory Audit
• Business Critical Reference Data
• Source Data
Time
Value

Cold
Data
Hot
Data
• Data for immediate use – daily, hourly
etc reporting – direct from
source/stream
• Frequently changed
• Pareto principle 90% or queries access
only ~ 20% of data
• High potential candidate for in-
memory
Warm
Data
• Next ~20% of data
• Recent history
• Less frequently changed
• Good candidate for standard
columnar store
• Possibly flash/SSD storage
• Infrequently used data
• Never/Rarely updated
• Archive storage
• Good candidate for Hadoop,
traditional RDBMS, tape or other
offline
• Transparent Query
Processing
• Cross-store Optimizer
Data Temperature

Data Temperature Analysis - Example
• Captured by analysis of database statistics
• Can also be captured via audit reports from BI tools
• Business impact analysis

Not All Technologies are Created Equal
Who is how columnar?
Vendor/Product Columnar Maturity
Teradata Database 2
Oracle Exadata 1
SAP HANA 3
Pivotal Greenplum/HAWQ 2
IBM DB2 BLU 3
Microsoft SQL Server xVelocity 2
HP Vertica 3
Actian Paraccel 3
IBM Netezza n/a
SAP Sybase IQ 3
Infobright 1
Vectorwise 1+
Level 1 Columnar: Uses PAX to achieve columnar compression. No columnar projection provided. No
columnar engine provided. Approximate 4X performance advantage over row store for read queries (10X
column compression versus 2.5X row compression).
Level 2 Columnar: Uses columnar compression and projection. No columnar engine provided. Approximate
10X advantage over Level 1 read queries (10% of the columns are selected).
Level 3 Columnar: Uses columnar compression and projection… and includes a columnar engine that
optimizes processing. Approximate 50X advantage over Level 2 read queries (Vector processing – 20X, SIMD –
8X, Fewer CPU Stalls – 2X, Cache Utilization – 10X, in-memory compression + projection 20X in differing
combinations for each query)

Not All Technologies are Created Equal
Who is how parallel?
Product Version/HW
Cores per
Node
UoP per
Node
Notes
Teradata EDW 6700H 16 32 Uses hyper-threads.
Greenplum DCA UAP Edition 16 8 Recommends 1 Segment instance for each 2 cores.
Exadata X3 12 24-Dec Maybe only 12… cannot find if they use hyper-threads.
PureData Striper 16 16 May use hyper-threads but limited by 16 FPGAs.
HANA Any Xeon E7-4800 40 80 Uses hyper-threads.
A unit of parallelism (UoP) is defined as the maximum number of instructions that can execute in parallel on a single
node for a single query. Since any program can start threads that wait I do not count these as UoP. On
some CPUs vendors such as Intel allow two threads to execute instructions in-parallel in a core. This is called hyper-
threading and, if implemented, it allows for two UoP on a single core.

Cost Considerations
Cost
SAP HANA $$$$
Microsoft SQL 2014 $$
Oracle TimesTen $$
IBM DB2 BLU $$
Teradata $$
Tableau $
Qlik $
Spotfire $$
TM1 $
Dynamic Cubes $
Hadoop ¢

You Don’t Have to be Moneybags…

…to Take Advantage of In-Memory

• For the right business problem, in-memory can truly
be a game changer
• New competitive differentiation through the implementation
of new/better business processes
• Creation of real-time applications combining analytics and
transactional information at the point of impact
• In-memory technology is now mainstream and
proven, and will move increasingly to de facto status
• As with any technology, the business value and use
case should drive the adoption of a specific
technology
Implications, Predictions, Considerations, Musings

• While expensive, given competition and
simultaneous growth in capacity and precipitous
price drops, in-memory technology will become
increasingly attractive for a growing number of use
cases
• Mobile/social/sensor data can be processed fast
enough in-memory then be persisted as needed
Implications, Predictions, Considerations, Musings

• While powerful, in-memory will add the most benefit
to properly architected applications
• Don’t just speed up bad decisions or faulty processes
• Use the right tool for the right job
• Doing an upgrade? It might be time to consider in-
memory
• In-memory is so fast – we’re talking orders of
magnitude – so don't POC for incremental speed!
We would be happy to help you evaluate the most
appropriate options for your specific environment
Senturus Recommendations

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BI Assessment
• Comprehensive Review of BI Stack Components: Server and Application Layer,
Data & Transformation Layer, and BI Tools Layer
• Deliverables include Grading and Roadmap for each of the Stack Components
• Cost Starting at $9,995 $8,995 before May 27
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Contact info@senturus.com or 888.601.6010 ext. 85
Senturus BI Assessment And Consultation

• Rob Klopp “Database Fog Blog”
• Deloitte In-Memory White Paper
• Oracle Columnar Store Video
• Microsoft SQL 2014 In-Memory Video
• Simple and Fast With IBM BLU Acceleration
• Information Week “In-Memory Databases”
• IBM Data Magazine – Is Your Big Data Hot or Cold?
• Gartner – Donald Feinberg Discusses In-Memory
• Senturus.com
– http://senturus.com/resources/
– mweinhauer@senturus.com or jfrazier@senturus.com
Additional Resources

www.senturus.com/events
Upcoming Events

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Cognos and Tableau Training Options

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Thank You!
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888-601-6010
info@senturus.com
Copyright 2015 by Senturus, Inc.
This entire presentation is copyrighted and may not be
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Senturus, Inc.

Demystifying In-Memory Technologies: Best Uses and Competitive Advantages for the Enterprise

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Demystifying In-Memory Technologies: Best Uses and Competitive Advantages for the Enterprise