MapR and Cisco Make IT Better

Robert Novak, Big Data Partners Consulting SE, Cisco
Bill Peterson, VP Partner Strategy, MapR
January 2017
It's No Use Going Back
to Yesterday's Storage Platform
for Tomorrow's Applications

Who is Bill and why is he here?
Today: Vice President of Partner Strategy MapR
Current:
Worldwide Partner Marketing
North America Field Marketing
Past:
Analyst (IDC)
PR Flack (PetersonPR, Page One PR)
Product Marketing (NetApp and more)
IT Manager (Harvard University)
Blogger at mapr.com
Tweeter at @thebillp

Who is Rob and why is he here?
Today: Consulting Systems Engineer
for Cisco’s Americas Partner Organization
Focused on big data and analytics
UNIX Sysadmin for ~20 years (retired)
Full stack: servers, storage, network, coffee
149 to 149k person companies
Sun, Nortel, 3PAR, eBay, Trulia, Disney, etc
“Big Data” herder since 2003
Hadoop admin (certifiable) since 2009
Cisco UCS C-Series admin since 2011 (early adopter!)
Charter Cisco Champion, VMware vExpert since 2013
Blogger at rsts11.com and Cisco Blogs
Tweeter at @gallifreyan and @rsts11 and @rsts11travel

‘I could tell you my adventures—beginning from
this morning,’ said Alice a little timidly: ‘but it’s no
use going back to yesterday, because I was a
different person then.’
1. Traditional app scaling
2. Moving into the Big Data Era™
3. Models of Scale and Density
4. MapR
• Next Gen Technologies
• Converged Data Platform (CDP)
• How is it being used
5. Cisco Unified Computing System (UCS)
• Utility Computing
• Policy-driven scalability
• S-Series storage servers
6. Scaling and sustaining with CDP on UCS
7. Where do we go from here?
What are we talking about today?

Traditional app scaling
(“Are you from the PAST?”)

Monolithic applications
Monolithic servers
Large somewhat-modular
storage arrays
Growing pains involve
forklifts
At the turn of the century…
By Peter Hamer - Ken Thompson (sitting) and Dennis Ritchie at PDP-11 Uploaded by Magnus
Manske, CC BY-SA 2.0, https://commons.wikimedia.org/w/index.php?curid=24512134

Moving into the Big Data Era™
(“We can’t stop for gas,
we’re late already!”)

Grid, Beowulf, Hadoop
Divide and conquer, scale
storage and compute
together
Hadoop started putting data
where it needed to be
Applications change, storage changes
Beowulf page public domain via Wikimedia Commons. Aiyara cluster By Kaewkasi at English Wikipedia, CC BY-SA 3.0,
https://commons.wikimedia.org/w/index.php?curid=52546611 . NASA cluster By NASA Ames Research Center/Tom Trower -
http://www.nas.nasa.gov/News/Images/columbia_3.html., Public Domain, https://commons.wikimedia.org/w/index.php?curid=43593

Modern platform scaling
Match the compute and the storage to the
application, and not the other way around

Hadoop is great, if you can
re-architect your apps, or
start from scratch
Access to the cluster
transparent only to
native apps
Ain’t nobody got time for that
Modern big data still requires virtual forklifts

What if…
Your cluster provided industry-standard
access methods like NFS
Existing applications could plug right in
with no recoding
New applications could live side by side

With Cisco
Unified Computing System
and MapR
Converged Data Platform…
You can.
Amazing but true.

© 2016 MapR Technologies 13© 2016 MapR Technologies 13© 2016 MapR Technologies
Driving Business Transformation with Data
How the MapR Converged Data Platform Drives Innovation, Growth and Efficiency

© 2016 MapR Technologies 14© 2016 MapR Technologies 14
We will disrupt ourselves
through Data
JPMorgan Chase

Industry Leaders Are Investing in Disruptive Technology Now
Legacy technology investmentNext-Gen technology investment
Source: IDC, Gartner; Analysis & Estimates: MapR
Next-gen consists of cloud, big data, software and hardware related expenses
(80,000)
(40,000)
-
40,000
80,000
120,000
2013 2014 2015 2016 2017 2018 2019 2020
$ (millions) Investment in Next-Gen vs. Legacy Technologies for Data
Total $ growth of IT market
Innovating and Reducing Costs at the Same Time
90% of data is on
next-gen technology
in just four years

MapR is Transforming Business with Data
WHAT
WE DO
Bring together
analytics and operations
into next-generation
Converged Applications
for the business
WHY
IT MATTERS
Empowers companies
to grow revenue
through innovation
and cutting costs
HOW
WE DO IT
Patented technology
architecture with the
world’s only complete
Converged Data
Platform
Leading companies around the world are transforming their
business with the industry’s only Converged Data Platform

Enabling Transformation Through Converged Applications
OPERATIONAL
APPLICATIONS
Immediate
ANALYTICAL
APPLICATIONS
Historical
Complete access to real-time and historical data in one platform
Converged Applications

Powered by the World’s Only Converged Data Platform
Breakthrough Reliability
Operate globally at enterprise
grade for mission critical apps
Breakthrough Value
Radically cut
costs of big
data IT
infrastructureBreakthrough
Innovation
Enable continuous
innovation with proprietary
technology and open
source access
A platform engineered to support next-generation
applications

Optimized for Speed
• Optimized compute engines
• High speed ingest & I/O
• Fast recovery and snapshots
• High scale elasticity
MapR Platform Lowers Friction For Next Gen App Developers
Innovative architecture delivers uncompromising scale, speed and availability
Optimized for Availability
• Global reliability
• Inter-cloud, Containerized
• Data protection & recovery
• Strong security model
Optimized for Scale
• High scale data store
• Document Database
• Persistent Streaming
• Schema Free SQL engine
• Community innovation

MapR Converged Data Platform
Product Line

Flexible processing where
change is the norm
Distributed processing across clusters, data
centers, public & private cloud environments
Supports global apps that
can scale arbitrarily
A Single Platform: On-Premises, In the Cloud, or Hybrid

We Make It Easy to Get Started
1
Understand
capabilities of big
data platform
Experimentation
2
Develop
first use cases
and put into
production
Implementation
3
Expand
to multiple use
cases across key
lines of business
Expansion
4
Integrate and expand
data driven apps and
analysis to all lines of
business and more
business functions
Optimization
Take the MapR Big Data Maturity Model

A Crisis of Complexity
Expensive to stitch together
Fragile not agile
“Connected” and “Federated” not converged
Limited in scale, no global
Many security models, points of failure
Hadoop
& Spark
cluster
Cassandra
for event
or content
logging
Classic data
warehouse
Message
middleware
Application
serverDocument
JSON DB
Search
server
vs. the Complete Data Platform
Engineered as single platform
Powers legacy and next-gen apps
Enables continuous innovation
Supports all big data technologies
Multiple deployment environments
BUSINESS MODEL: SUPPORT FREE SOFTWARE BUSINESS MODEL: ENTERPRISE SOFTWARE LICENSES

Converge, Transform and Grow with MapR
Innovate
for Growth
Realize
Cost Savings

Our Customers Are Leading the Way
Financial Services Telco & Media
Ad tech
Government
RetailOver 80 use cases including
payment efficiency and accuracy
of claims processing.
$2M/month reduction in
payment errors and fraud.
Provides 95% of Fortune 500
CPG and retailers with data
and analytics. Achieved
$2.5M/year annual savings
from mainframe & DW offload.
Ported credit scoring use case
to MapR resulting in 20X cost
savings over DB2.
Biometric identification system
for more than 1.25 billion
people in India. $1.3B yearly
savings thru fraud reduction.
Developed a new self-service
analytics platform to give their
customers better market
insights to help them
operationalize their decisions.
Protects $1 trillion in charge
volume from fraud every
year. Amex offers program
has saved card members
over $180M.INNOVATION
COST
REDUCTION

© 2016 MapR Technologies 28
MapR Performance Advantage: Summary
YARN Tasks
Benchmark: Terasort
Measurement: Total time (s)
Distributed Filesystem
Benchmark: DFSIO
Measurement: Read/Write MB/s
NoSQL Database Ops
Benchmark: YCSB 50/50
Measurement: Ops/s/client
Test configuration: 10 nodes, 16x2 cores (2.6 GHz), 11x1TB disk (7200 RPM, 1 SP), 128G RAM, jumbo frames, 1x10GE
Double the
speed for YARN
Up to 2X faster
distributed I/O
0 10000
Other Vendor
MapR 5.0
Operations per second
3-4X more NoSQL
throughput
+ True Read/Write Distributed Filesystem
0 500 1000 1500 2000
Other Vendor
MapR 5.0
YARN Jobs Complete in Half the Time
0 500 1000 1500
Other Vendor
MapR 5.0
Write MB/sec
write
read

MapR Leads TPC-HS Benchmark for 1TB, 10TB Sizes
• Cisco + MapR were the first to complete the Big Data benchmark from the
Transaction Processing Council in 2015
• MapR 5.x recorded the highest performance for the 1TB and 10TB tests
• “… stresses both hardware and software including Hadoop run-time,
Hadoop Filesystem API compatible systems and MapReduce layers”

File Creation Benchmark
MapR Scales to Billions of Files, Competition Fails at 1.25M
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
0 2000 4000 6000
Filecreates/s
Files (M)
Other distribution
fails
Test Configuration:
100 byte file creation, 10
nodes, each w/2 x 4 cores,
24G RAM,
12x1TB (7200 RPM), 1x10GE
0
100
200
300
400
0 0.5 1 1.5
Filecreates/s
Files (M)
Other distributionMapR Other Advantage
Rate
(creates/s)
14-16K 335-360 40x
Scale (files) 6B 1.3M 4615x
platform maintains a consistent high rate of file creation

World Record Performance
PREVIOUS RECORD:
1.6 TB with 2200 nodes
1.65 TBIN 1 MINUTE
298 NODES
NEW MINUTESORT WORLD RECORD
With a Fraction of the Hardware
Hardware Specs: Cisco UCS C240M3S
2x Intel ® Xeon ® E5-2665 @ 2.4GHz (16 cores) CPU; 128 GB RAM; SATA 24x1 TB Storage; Cisco VIC 10 GbE NIC
2200 nodes - competitor
1/7th of hardware required
298 nodes -

Cisco UCS
for Scalable Platforms

Hardware Still Matters
A quick glance at infrastructure for Big Data
a.k.a. Why is Cisco here?

Why does hardware still matter?
34
• Cisco customers’ big data pools tend to grow 2-3x/year (or
more… lots more)
• Customer IT staff doesn’t grow as fast
• The Cisco Unified Computing System (UCS) provides
scalable, repeatable, predictable, and manageable
deployments across dozens to thousands of servers for any
application deployment
• Pallet to production in hours, not days or weeks
• Deep engineering integration between Cisco and MapR with
tested and proven configurations
More on this later…

Ten years from now, tech industry
historians will remember at least two
things about 2009:
the economic mess and the Cisco
UCS announcement.
If nothing else, Cisco just made the
industry much more exciting than it
was last Friday.
Jon Oltsik, CNet, March 16, 2009
Why Cisco UCS for any hardware deployment?
Ten second edition
• Scalability
• Manageability
• Performance

Ten years from now, tech industry
historians will remember at least two
things about 2009:
the economic mess and the Cisco
UCS announcement.
If nothing else, Cisco just made the
industry much more exciting than it
was last Friday.
Jon Oltsik, CNet, March 16, 2009
Why Cisco UCS for any hardware
deployment?
 Single point of management and access control
for thousands of servers
 Centralized host/network/storage/lights-out
firmware management built in
 High performance networking at the core
 Flexible network configuration with vNICs for
security and scalability
 Open XML API for automation and third party
integration
 Fully functional remote console (including virtual
media) at no extra cost

Why do these especially matter for highly scalable
platforms?
 Single point of management and access
control for thousands of servers
 Centralized host/network/storage/lights-out
firmware management built in
 High performance networking at the core
 Flexible network configuration with vNICs for
security and scalability
 Open XML API for automation and third party
integration
 Fully functional remote console (including
virtual media) at no extra cost
 Big Data grows faster than most platforms.
Ever added 100 Oracle servers?
 Big Data environments tend to grow 2x-3x
(or more) within two years. IT staff do not.
 More data moving around means heavier
pressure on the network to perform
 New software models may require different
networking and storage
 Larger companies have existing
management infrastructures to work with
 Some vendors nickel and dime for
management features and licenses.

Cisco UCS Reference Architectures
• Integrated Infrastructures for Big
Data (a.k.a. CPAv4) updated
May 2016
• Proven, predictable configs to
start from and grow with
• Scale to petabytes of data with
high performance and low TCO
• Seven infrastructure designs for
different compute/capacity/
performance requirements
• CPAv4 Blog link

Cisco UCS Integrated Infrastructure for Big Data
4th Generation of Reference Architectures and Bundles
UCS-SL-CPA4-S UCS-SL-CPA4-H UCS-SL-CPA4-P1 UCS-SL-CPA4-P2 UCS-SL-CPA4-P3 UCS-SL-CPA4-C1 UCS-SL-CPA4-C2 Extreme Capacity
Coming Soon
Network: 2x 6248
Servers: 8 X UCS-BD-
C220M4-S1
Server Type: C220 M4 SFF
CPU: 2x 2620v4
Memory: 128GB DDR4
Drives: 8 x 1.2TB 10K SAS
HDD
VIC: VIC 1227
RAID: 12Gps SAS, 2GB
UCSD: no
Cores: 128
Memory: 1024
Raw Storage: 76.8
I/O Bandwidth: 7.5
Gbytes/sec
Network: 2x 6332
C220M4-H1
CPU: 2x 2680v4
Memory: 256GB DDR4
Drives: 8 x 960GB SSD
VIC: VIC 1387
UCSD: no
Cores: 224
Memory: 2048
Raw Storage: 60
I/O Bandwidth: 20
Gbytes/sec
Network: 2x 6296
C240M4-P1
CPU: 2x 2680v4
Memory: 256GB DDR4
OS: 2 x 240GB SSD
HDD
VIC: VIC 1227
UCSD: yes
Cores: 448
Memory: 4096
Raw Storage: 460.8
I/O Bandwidth: 45
Gbytes/sec
Network: 2x 6296
C240M4-P2
CPU: 2x 2680v4
Memory: 256GB DDR4
OS: 2 x 240GB SSD
HDD
VIC: VIC 1227
UCSD: yes
Cores: 448
Memory: 4096
Raw Storage: 691.2
I/O Bandwidth: 48.75
Gbytes/sec
Network: 2x 6332
C240M4-P3
CPU: 2x 2680v4
Memory: 256GB DDR4
OS: 2 x 240GB SSD
HDD
VIC: VIC 1387
UCSD: yes
Cores: 448
Memory: 4096
Raw Storage: 691.2
Gbytes/sec
Network: 2x 6296
C240M4-C1
Server Type: C240 M4 LFF
CPU: 2x 2620v4
Memory: 128GB DDR4
OS: 2 x 240GB SSD
Drives: 12 x 6TB 7.2K SAS
HDD
VIC: VIC 1227
UCSD: yes
Cores: 256
Memory: 2048
Raw Storage: 1152
Gbytes/sec
Network: 2x 6296
C240M4-C2
Server Type: C240 M4 LFF
CPU: 2x 2620v4
Memory: 256GB DDR4
OS: 2 x 240GB SSD
Drives: 12 x 8TB 7.2K SAS
HDD
VIC: VIC 1227
UCSD: yes
Cores: 256
Memory: 4096
Raw Storage: 1536
Gbytes/sec
Network: 2x 6332
Servers: 9 X UCS-BD-C3220-
HC1
Server Type: C3260 (2 x
servers)
CPU: 2 x 2680v4
Memory: 256GB DDR4
OS: 2 x 240GB SSD
Drives: 424 6TB 7.2K SAS
HDD
VIC: VIC 1387
UCSD: yes
Cores: 504
Memory: 4608
Raw Storage: 2544
Gbytes/sec

High performance fabric
for distributed storage Automation for rapid
scalability
Capacity / performance
versatility for all apps
API controls for
developers
reduction in
provisioning time83% reduction in ongoing
management costs62% reduction in power
and cooling costs49% Reduction
in cabling78%
UCS: Ideal for Active Data
UCS Customer Results

Cisco UCS S3260: Modular Platform
Massive Capacity
• 600TB data storage
capacity in 4U
• Up to 90TB SSD Flash
Lower
Capex34%
Multi-Node Power
• Single or Dual Server Options
• New cache acceleration
capabilities with NVMe and
Fusion IoMemory
I/O flexibility
• 40Gb Cisco Virtual Interface
Card (VIC) Technology
• 256 virtual adapters per node
plus 16Gb native Fabre Channel
options
Total Automation
• Scale to Petabytes in
minutes with UCS Manager
• Cisco SystemLink
Technology with flexible
storage profiles
Lower Ongoing
Management80% Less
Cabling70% Less
Space60% Lower
Power59%
Significant Benefits Compared to Conventional 2RU Servers

Versatility for All Data-Intensive Applications
Object Storage
• Email Storage
• Medical imaging
• Video Storage
600 Terabytes Raw Storage
1G or 4G RAID Cache
Capacity Play
Data Protection
• Consolidated backup target
• Multi-site replication
• Disaster Recovery
160 GB Aggregate VIC I/O
8 and 16GB Fiber Channel PCIe
I/O Flexibility
Big Data Analytics
• Recommendation engines
• Fraud detection
• Network security
1.6TB NVMe
2 x Fusion ioMemory3 PX
90 TB SSD Flash
Cache Optimized
Content Distribution
• Video Surveillance
• Facial Recognition
• Content Delivery
Dual Server Nodes
72 CPU Cores
Compute Optimized

• Updated by Cisco and MapR engineers
in October 2016
• 250+ page guide to design
and deployment, pallet
to production
• Based on UCS C-Series (C220, C240,
S3260) servers and MapR Converged
Data Platform
• Download for free at
cisco.com/go/bigdata_design
• MapR + UCS S3260 CVD (PDF)
• MapR Streams CVD (HTML/PDF)
Cisco Validated Designs (CVD) for MapR

• CVD Updated November 1, 2016,
to optimize for S-Series
• CVD for SAP HANA on MapR Converged Data
Platform released June 2016
• Flexible server and storage offering means
you can plug it into whatever you need to
do, CVD or reference architecture or not
What’s coming for Cisco and MapR?

Call To Action
MapR & Cisco Make IT Better Webinar & Roadshow Series
– It's No Use Going Back to Yesterday's Storage Platform
for Tomorrow's Applications
– Cisco & MapR for SAP HANA
– Cisco & MapR for Software-Defined AND Web-Scale Storage
Read the CVD’s (or at least the parts that interest you)
Follow: @gallifreyan
– @mapr
– @Cisco
– @thebillp
Attend Cisco Live, Big Data Everywhere, Strata Hadoop World

Cisco UCS S3260 System Overview
Drives
4 Rows of Hot-Swappable HDD
4TB/6TB/8TB/10TB with up to 2 Rows
of 400GB/800GB/1.6TB/3.2TB SSD
Total Top Load: 56 drives
FAN
8 Hot-Pluggable
Fans
Server Node
Up to (2) Based on Intel V4 CPUs, LSI
12G RAID, Up to 512GB DDR4 RAM
(1024GB Post-FCS), and NVMe
Optional Second Node
Server Node or Drive Expansion
or PCIe Expansion
Up to (4) 120GB/480GB/1.6TB SSDs
HW RAID, Hot-Plug, OS/Boot
System I/O Controller (SIOC)
Up to (2) Cisco VIC 1300 on Chip
Power Supply
4 Hot-Pluggable PSUs
*Shown with Single Server Node
and IO Expander

What’s new (from C3260)
M4 Server Node
Dual-Socket Intel XEON E5-2600 V4 Processors
Up to 512GB of DDR4 memory
Single 800G or 1.6TB 2.5" NVMe SSD
12G SAS RAID with 4GB Cache
I/O Expander Module
Dual 8x PCIe half-height half-width slots
Support for 3rd party add-in Modules
Works only with M4 server nodes
Connectivity
16G FC8/16G FCDual-
10GbE
Quad-
1GbE
Flash Memory
64000GB32000GB10000GB

UCS S3260 Management
• UCSM 3.1.2 Integration
 Fully Managed by 2nd and 3rd
Generation Fabric Interconnects
 Connects via FI Server Ports to
3260 SIOC Ports
 Each 3260 Physical Box is a
Chassis
 Chassis-Wide and Per Server
Node Management
 Inventory, Compute and Storage
Configuration, FW Mgmt, Pools,
Policies, Profiles, Templates,
vNICs/vHBAs, and Much More
 Storage Profiles – Disk Group
(RAID) and LUN Configuration

MapR and Cisco Make IT Better

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