You’re not the only one still loading your data into data warehouses and building marts or cubes out of it. But today’s data requires a much more accessible environment that delivers real-time results. Prepare for this transformation because your data platform and storage choices are about to undergo a re-platforming that happens once in 30 years.
With the MapR Converged Data Platform (CDP) and Cisco Unified Compute System (UCS), you can optimize today’s infrastructure and grow to take advantage of what’s next. Uncover the range of possibilities from re-platforming by intimately understanding your options for density, performance, functionality and more.
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MapR and Cisco Make IT Better
1. 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
2. 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
3. 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
4. ‘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?
6. 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
7. Moving into the Big Data Era™
(“We can’t stop for gas,
we’re late already!”)
8. 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
10. 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
11. 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
34. 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…
35. 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
36. 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
37. 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.
38. 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
39. 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
Servers: 8 X UCS-BD-
C220M4-H1
Server Type: C220 M4 SFF
CPU: 2x 2680v4
Memory: 256GB DDR4
Drives: 8 x 960GB SSD
VIC: VIC 1387
RAID: 12Gps SAS, 2GB
UCSD: no
Cores: 224
Memory: 2048
Raw Storage: 60
I/O Bandwidth: 20
Gbytes/sec
Network: 2x 6296
Servers: 16 X UCS-BD-
C240M4-P1
Server Type: C240 M4 SFF
CPU: 2x 2680v4
Memory: 256GB DDR4
OS: 2 x 240GB SSD
Drives: 24 x 1.2TB 10K SAS
HDD
VIC: VIC 1227
RAID: 12Gps SAS, 2GB
UCSD: yes
Cores: 448
Memory: 4096
Raw Storage: 460.8
I/O Bandwidth: 45
Gbytes/sec
Network: 2x 6296
Servers: 16 X UCS-BD-
C240M4-P2
Server Type: C240 M4 SFF
CPU: 2x 2680v4
Memory: 256GB DDR4
OS: 2 x 240GB SSD
Drives: 24 x 1.8TB 10K SAS
HDD
VIC: VIC 1227
RAID: 12Gps SAS, 2GB
UCSD: yes
Cores: 448
Memory: 4096
Raw Storage: 691.2
I/O Bandwidth: 48.75
Gbytes/sec
Network: 2x 6332
Servers: 16 X UCS-BD-
C240M4-P3
Server Type: C240 M4 SFF
CPU: 2x 2680v4
Memory: 256GB DDR4
OS: 2 x 240GB SSD
Drives: 24 x 1.8TB 10K SAS
HDD
VIC: VIC 1387
RAID: 12Gps SAS, 2GB
UCSD: yes
Cores: 448
Memory: 4096
Raw Storage: 691.2
I/O Bandwidth: 48.75
Gbytes/sec
Network: 2x 6296
Servers: 16 X UCS-BD-
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
RAID: 12Gps SAS, 2GB
UCSD: yes
Cores: 256
Memory: 2048
Raw Storage: 1152
I/O Bandwidth: 26.25
Gbytes/sec
Network: 2x 6296
Servers: 16 X UCS-BD-
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
RAID: 12Gps SAS, 2GB
UCSD: yes
Cores: 256
Memory: 4096
Raw Storage: 1536
I/O Bandwidth: 26.25
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
RAID: 12Gps SAS, 2GB
UCSD: yes
Cores: 504
Memory: 4608
Raw Storage: 2544
I/O Bandwidth: 57.97
Gbytes/sec
40. 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
41. 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
42. 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
43. • 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
44. • 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?
45. 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
47. 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
48. 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
49. 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
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
https://commons.wikimedia.org/wiki/File:Beowulf.Kenning.jpg public domain circa 2006
https://en.wikipedia.org/wiki/File:Beowulf.firstpage.jpeg
https://en.wikipedia.org/wiki/Aiyara_cluster#/media/File:Aiyara-cluster-A0.jpg
Public domain By Wesso (Hans Waldemar Wessolowski) / Experimenter Publishing - http://www.philsp.com/mags/amazing_stories.html, Public Domain, https://commons.wikimedia.org/w/index.php?curid=42839174
Leaders in all industries, including JP Morgan Chase in financial services, are now turning to data to transform their businesses. If you don’t think it’s happening in your industry, think again.
Over the next four years, companies will experience flat IT spending. But underneath that will be a steady decrease in legacy spend accompanied by a corresponding increase in spend behind next gen technologies. But this chart also provides insight into the solution. The key to reducing costs while driving innovation is the data.
CLICK In fact, forecast also shows that within four years 90% of data will be on next gen technology….
MapR is transforming how leading companies around the world do business. We enable next generation converged applications through the patented technology architecture of the world’s only converged data platform. Companies can apply the platform to a broad range of activities that improve business whether through increasing revenues, reducing costs, or mitigating risks.
Regardless of where they start with the application of their data, our customers are able to develop a broad range of solutions, transforming their businesses through the development of what we call Converged Applications. Convergence enables the immediacy of operational applications with the insights of analytical workloads. They leverage continuous analytics, automated actions, and rapid response to impact business outcomes in real-time.
MapR has a unique platform engineered with all the essential technology to support operational and analytical applications at scale, transforming business with innovation, lower costs and the reliability required for mission critical applications.
With MapR convergence does not dictate centralized processing. We think you’ll need flexibility to manage your data across multiple environments, whether on-premise, in the cloud, at the edge, or in a hybrid model, so we designed our platform to support all these situations.
This processing model supports global apps that can scale arbitrarily, can be connected synchronously or asynchronously across locations and support data flow across two or more processing locations.
TECHNICAL DETAIL: MapR supports distributed clusters with wide-area replication, data and job placement control, logical volumes for separation of workloads, and data access.
And we make it easy to get started. We’ve developed a comprehensive maturity model that will help you quickly understand where your organization is on it’s big data journey and how you can best take advantage of the converged data platform.
With alternative technologies, all you will experience is what we call the crisis of complexity. Alternative offerings attempt to stitch together multiple point products, but result in lower performance, limited scale and higher costs. Don’t learn the hard way that “connected” or “federated” don’t provide the same level of performance and savings as convergence. There is no comparison. The patented MapR Converged Data Platform is the only solution engineered from the ground up to seamlessly work with all of today’s data types and technologies, meeting all of your business needs.
It’s time to transform your business. Converge, transform and grow the the world’s only Converged Data Platform.
Our customers demonstrate what’s possible only with the Converged Data Platform. They are now able to leverage their data to drive innovation and reduce costs.
This slide presents a summary of 3 top Hadoop benchmarks:
Terasort (on YARN, not MRv1)
DFSIO (also on YARN)
YCSB (50/50 workload)
Another great proof point is the MinuteSort benchmark which is currently “officially” held by Yahoo with 2200 nodes. This is a test which shows how much data you can sort in 1 hour
A MapR customer was adding 300 nodes to their MapR cluster and wanted to run some tests to see if they were getting good performance.
What they discovered is that they actually BEAT the world record for MinuteSort!
What’s more astounding is that they did this with 1/7th or 13% of the hardware. Imagine the cost advantage and how much more they could do with a similar sized cluster!
Cisco does servers? – quick answer
Cisco does big data? – almost-as-quick answer
What’s with Cisco and Splunk? – lead into next segment
Their servers are also available as reference architecture for specific workloads
The configuration can be used as is or use a template for creating larger configurations
How UCS (platform level, not just S-Series) is the blueprint for what you need here.
Fabric based infra (UCS) is best for scale-out, clustered storage approaches. “the data center fabric is the backplane for distributed applications”
UCS benefits (for bottom of slide)
83% reduction in provisioning times
62% reduction in ongoing management costs
49% reduction in power and cooling costs
78% reduction in cabling
1 network hop in UCS environment; pull an E/W latency stat from Egan’s team
Versatility of our portfolio to meet different ratios of performance or capacity
Show new storage-optimized system type (S-Series) along with blade and rack. Versatility of 3260 to index to performance or capacity
VO: You can easily bend sheet metal and cram in a bunch of drives but that won’t get you there
Massive 600TB data storage capacity
Scale to Petabytes in minutes with UCS Manager
New cache acceleration capabilities with NVMe
Flexible connectivity from Ethernet to Fiber Channel
Faster network access with 40Gb VIC support
Cisco BIDI transceivers enable 40G over 10G cabling
Flash math: 28 x 1.6TB SSD (vertical Mount) + 4 x 1.6TB SSD (rear-mount boot) = SSD @ 1.6TB