pptx - Oracle

Report
SAS on Oracle for Big Data and Cloud Services:
Insights into a Strong Partnership (CON8653)
Paul Kent, VP Big Data, SAS
Randy Wilcox, DBA Team Manager, SAS Solutions onDemand
Hermann Baer, Director Product Management, Oracle
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
AGENDA
•
Introduction
• SAS Visual Analytics, SAS High Performance Analytics
•
•
on Oracle Engineered Systems
Oracle & SAS Collaboration

Setting the Stage for Big Data
 Oracle Database 12c
•
SAS Solutions onDemand – SAS Cloud Services
3
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
 Reflection on a stronger partnership than ever

SAS High-Performance Analytics and SAS Visual
Analytics on Oracle Engineered Systems

Extensive engineering collaboration
 Sizing, configuration guidance and best practices for
deployment
 Support for POVs

A strong technology and business alliance to develop
solutions and products brings tremendous value and
confidence
4
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
OUR
PERSPECTIVE
Big Data is RELATIVE not ABSOLUTE
BIG DATA
ANALYTICS
BIG ANALYTICS
When volume, velocity and variety of data exceeds an
organization’s storage or compute capacity for accurate
and timely decision-making
The process surrounding the development, interpretation,
and useful application of statistics to solve a problem.
Analytics applied to data provides the 4th V = Value
Three types: Descriptive, Predictive, Prescriptive
The combination of using ANALYTICS on BIG DATA
AND/OR the capability to run advanced or complex
analytics on any size data.
6
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS® HIGHPERFORMANCE
ANALYTICS
KEY COMPONENTS
7
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
Oracle Engineered Systems I
Exadata Database
Machine
RDBMS storage
compression and
database
parallelization via
“Exadata Storage
Servers”
Exalogic Elastic
Cloud
Extreme -performance
I/O connecting large
amount of compute
power and memory
HARDWARE AND SOFTWARE
Oracle Virtualized
Compute
Appliance (OVCA)
VM Server
virtualization – runs
Oracle Linux, Oracle
Solaris, Windows.
Software Defined
Networking
Big Data
Appliance
Massive disk storage
array with highbandwidth I/O for
loading ‘big’ data
SPARC
SuperCluster
SPARC servers, highperformance I/O and
Exadata storage
servers in one rack
8
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
ANALYTICAL HOW ANALYTICAL LEADERS ARCHITECT TO EXPLOIT DATA
WORKLOAD
Analytics Platform
Analytical Services
Analytical Models and Rules Repository
Fast insights - IN-MEMORY
Analytical
Visualization
Event Data Store
EDW
Raw Data Pool
(HDFS / NoSQL)
Discovery
ANALYTICS
Inc. Enterprise Miner
Event Data Store
(RDBMS)
Analytical
Data
Warehouse
Tx Data Sources
Event Management Platform
Event
Streams
C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
Event Stream
Processing
R/T Decision Services
Operational
Execution
SAS BUSINESS ANALYTICS FRAMEWORK
Analytic Data
Warehouse / Marts
SAS Analyst’s
Desktops
SAS Compute
Server
SAS Metadata
Server
Relational
Data Store
Web Application
Server
Data Tier
Server Tier
Metadata Tier
Copyright © 2012, SAS Institute Inc. All rights reserved.
Web Tier
SAS Web Clients
Client Tier
SAS BUSINESS ANALYTICS FRAMEWORK
Analytic Data
Warehouse / Marts
SAS Analyst’s
Desktops
SAS Compute
Server
SAS Metadata
Server
Relational
Data Store
Web Application
Server
Data Tier
Server Tier
Metadata Tier
Copyright © 2012, SAS Institute Inc. All rights reserved.
Web Tier
SAS Web Clients
Client Tier
ANALYTICAL RAPID TIME TO VALUE IN STANDARD DEPLOYMENT
WORKLOAD
Analytical Services
(on Oracle Exalogic / Big Data / OVCA )
Analytical Models and Rules Repository
SAS Grid-in-a-Box
SAS Visual
Analytics
Inc. Enterprise Miner
Big
Data
(HDFS
/ NoSQL)
SAS
Appliance
Big Data
Connector
Oracle Event
Processing
SAS Event
Stream
processing
Oracle
Business
Rules
SAS Business
Rules
manager
Oracle Policy Automation
C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS High
Performance
Analytics
SAS ANALYTICS
SAS Visual
Statistics
Exadata
SAS
Analytics
Accelerator
Database &
options
SAS Enterprise Decision
Management
Real-Time Decisions
SAS BUSINESS ANALYTICS FRAMEWORK
SAS Compute
Server
Analytic Data
Warehouse / Marts
SAS Analyst’s
Desktops
SAS Metadata
Server
Relational
Data Store
Infiniband
Data Tier
Server Tier
Web Application
Server
Metadata Tier
Web Tier
Copyright © 2012, SAS Institute Inc. All rights reserved.
SAS Web Clients
Client Tier
SAS BUSINESS ANALYTICS FRAMEWORK
SAS Compute
Server
Analytic Data
Warehouse / Marts
SAS Analyst’s
Desktops
SAS Metadata
Server
Relational
Data Store
Web Application
Server
Infiniband
Metadata Tier
Data Tier
Server Tier
Web Tier
Copyright © 2012, SAS Institute Inc. All rights reserved.
SAS Web Clients
Client Tier
SO….
HOW DOES IT GET SUCH GOOD PERFORMANCE?
16
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
HOW DOES IT WORK EXALOGIC/BDA/OVCA (COMPUTE) WITH EXADATA (STORAGE)
Exadata
Exalogic / BDA / OVCA
Client
17
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
HIGH-PERFORMANCE
ANALYTICS
•
Using Different Data and Computing Appliances with Asymmetric HPA
•
Computing Appliance (Exalogic/BDA/OVCA)
SAS Server
General
Captains
TKGrid
TK
TK
TK
libname a oracle
server=“dataAppliance”;
proc hpcorr data=a.flights;
performance
mode=asym
host=“computingAppliance”;
run;
Access
Engine
Data Appliance
(Exadata)
Controller
Workers
18
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
HIGH-PERFORMANCE
ANALYTICS
•
Using Different Data and Computing Appliances with Asymmetric HPA
•
Computing Appliance (Exalogic/BDA/OVCA)
SAS Server
General
Captains
TKGrid
TK
TK
TK
libname a oracle
server=“dataAppliance”;
proc hpcorr data=a.flights;
performance
mode=asym
host=“computingAppliance”;
run;
Access
Engine
Data Appliance
(Exadata)
Controller
Workers
19
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS High-Performance Analytics Performance
SAS EP Parallel Data Feeders
DOP=1
DOP=24
DOP=24
(flash cache)
Add(5)
1.25min
1.5min
.5min
Add(20)
2.5min
1.5min
.5min
Add(100)
13min
1.5min
.6min
Add(200)
16min
~2min
1.25min (10x)
Table 1: Summation of 5/20/100/200 columns;
Baseline: DOP=1 (no parallelism)
120M rows, 400 columns, reg_simtbl_400
20
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS High-Performance Analytics Performance
SAS EP Parallel Data Feeders
Access
Access /
DBSlice
SAS HPA
Using EP
Reg_sim_200
1:01:12
0:28:37
0:08:00
Reg_sim_400
1:49:11
0:55:33
0:16:05 (7x!)
Table 2: Scan times for 2 tables
(200 columns, 400 columns, 120M rows);
Baseline: SAS/ACCESS vs. HPA EP feeder
21
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS HIGH PERFORMANCE ANALYTICS, SAS VISUAL ANALYTICS
ON ORACLE ENGINEERED SYSTEMS
Big Data Appliance (BDA)
SAS Analyst’s
Desktops
bda101
bda102
bda103bda118
SAS HighPerformance
Analytics Server
Root Node
SAS Visual
Analytics Server
Tier
SAS Visual
Analytics Middle
Tier
SAS LASR InMemory Analytics
Server
Hadoop
Namenode
Hadoop
Datanode
Hadoop
Datanode
SAS Web Clients
22
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS AND ORACLE
WORKING TOGETHER TO CREATE CUSTOMER VALUE
Analysis Platform
Platform
Requirements
Readiness
Analysis
• Joint R & D development and
Product Management teams
in Cary and Redwood
Shores
• Focus on driving SAS
technology components to
run natively in Oracle
database
• Joint performance
engineering optimizations
C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
Analytics 3.0
Data
Model and
Acquisition
Analyse
• Template physical architectures
developed based on use-cases
• Physically tested and
benchmarked together
• Reduction in physical effort
• Overall reduction in lifecycle
costs
Lifecycle Management
Deployment /
Expand
Monitor
• Best Practice papers
• SAS and Oracle Engineers
provide joint "Sizing and
Architecture Analysis and
Design"
SAS AND ORACLE
BETTER TOGETHER
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS® EXADATA VALUE PROPOSITION
Randy Wilcox, DBA Team Manager, SAS Solutions onDemand
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS SOLUTIONS
OVERVIEW
ONDEMAND
SAS Solutions OnDemand – Started in 2000, 450 global staff members
• Advanced Analytics Lab (AAL) – Created in 2007
• Over 1 PB of data under management
• Multiple ASP lines of business, representing over 400 customer sites (5 - 30,000 users
per solution) in more than 70 countries
•
•
•
•
Experience supporting customers with unique situations
•
•
•
Retail, financial services, health care, pharmaceutical, government, entertainment analytics
Marketing and fraud analytic solutions
Regulatory constraints - AML, FDA, HIPAA, Safe Harbor, SOC 2 / SOC 3
Working with multiple parties
Best Practices
•
•
Innovative techniques
Documented processes and procedures
26
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS SOLUTIONS
ADVANCED ANALYTICS LAB
ONDEMAND
•
Formed by CEO Jim Goodnight in 2007
• Premier analytic services group
• Mission:
•
Develop Innovative analytical processes and techniques, using SAS software, to
solve our customers' high end business problems.
• Support sales and consulting in generating revenue by helping close analytically
challenging engagements
• Produce analytical work products for repeatable processes
•
98% AAL members with graduate degrees in analytic fields (34% Ph.D.'s)
• 20 approved and 10 pending patents
• Learn with the experts to the degree desired
27
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
STAFFING TO SUPPORT ANY CUSTOMER NEED
•
•
•
•
•
•
•
•
•
•
Analyst
•
Application Developer
•
Business Analyst
Compliance Specialist
•
Data Architect / Data Modeler •
Data Custodian
•
Data Integration Consultant •
Database Administrator
•
Information Technology
•
System Administrator
•
Instructional Designer
•
Load Tester
Operations / Maintenance
Engineer
Performance Analyst
Program Manager
Project Manager
Quality Assurance Analyst
Quality Specialist
Release Manager
Repository Administrator
Retail Duty Manager
•
•
•
•
•
•
•
•
•
•
•
Retail Operational Manager
SAS Administrator
Service Desk Consultant
Solution Architect
System Administrator
Technical Account Manager
Technical Architect
Technical Communicator
Technical Lead
Trainer
Web Developer
28
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS SOLUTIONS
EXADATA AT SAS SOLUTIONS ON DEMAND
ONDEMAND
Benefits
Multitenant
Agility
Business Benefits
SAS Solutions OnDemand utilizes key
features of Exadata: Multitenant, Agility, and
Performance to consolidate, speed time to
deployment and drive down cost while
realizing performance improvements
Business Objectives
• Maximize Investment – multitenant
DW/BI
• Consolidation of servers
•
Deployed quarter racks in
multiple data centers
•
Utilized ZFS Storage Appliance
for a backup solution
• Reduce overall TCO
• Prepare for exponential data growth
• Faster customer time-to-cost
recovery
Solution
• 2012: consolidate 15+ customer
deployments to Oracle Exadata
• 2013: Addition of new customers to
Oracle Exadata
29
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS SOLUTIONS
CHALLENGES
ONDEMAND
Key Problems with Legacy environment:
•
•
•
•
•
•
Low CPU utilization – typical usage <20%
Complex server farm
Under-utilized licenses
High energy cost with legacy servers
Systemic inefficiencies
Requires support and coordination from multiple internal organizations
and vendors
30
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS SOLUTIONS
EXADATA KEY REQUIREMENTS
ONDEMAND
Multitenant:
• Consolidation of database instances to
Exadata
• Utilize multiple hosted Exadata racks
• Instance caging
• Maintain separation of data across
customers
Agility:
• Decrease deployment time
• Remove dependencies on other
departments
• Oracle DB License Consolidation:
• Consolidate under utilized licenses
• Lower yearly license spend
• Performance Improvement:
• Not an initial key requirement but
have recognized significant
performance improvements
Business Continuity:
• High availability SLA’s >99%
• Superior backup, restore, and recovery
31
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS SOLUTIONS
MULTITENANT BENEFITS
ONDEMAND
BEFORE
Current
Many Disparate
Customer Systems
Consolidated on
Exadata
Exadata X2-2
DB
Consolidation
Data Guard
Data Guard
• Production
PROBLEMS:
Typical usage <20%
Costly
Inefficient
BENEFITS:
• High availability
• Cloud control/OEM 12c
• Lowered cost of license per CPU for
database
• Exadata could handle the spike and meet
SLA
• Optional compress data using HCC to lower
costs and no impact on performance
• Backup / recovery configured once
• Less data center storage space used
• Lower energy consumption to host
• Total cost of ownership significantly lowered
• Test and QA
• Disaster
Protection
32
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS SOLUTIONS ON
AGILITY BENEFIT
DEMAND
IT Team
Network
Team
Single
DBA
team
Storage
Team
Backup
Team
33
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS SOLUTIONS ON
AGILITY BENEFIT
DEMAND
Key Recognized Benefits:
• Onboarding a new database went from days to hours
• OEM12c Cloud Control to manage the entire stack
• The DBA team size is able to complete the entire
process
• Storage, network, hardware and OS setup steps
eliminated
• Dependency on corporate backup/recovery services
was reduced to DR only with the usage of ZFS
• TCO decreased for hosting services
Enhanced
Business
Performance:
Service Levels:
Improved and
consistent delivery
to the business
Innovation:
Superior capabilities
to drive high value
business results
Time to Value:
Reduced time to
stand-up and deliver
database services
34
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
CUSTOMER EXAMPLE ONE: ANTI-MONEY LAUNDERING
CURRENT ENVIRONMENT
BEFORE
Customer has 8
core dedicated
standalone
Customer uses
1730 GB
•
•
Up to 45x performance increase with Exadata
storage indexes
Significant reduction in storage by utilizing
Hybrid Columnar Compression on aging
partitions
CURRENT
1- ¼ RAC Exadata X2-2
Each ¼ RAC has:
2 db nodes / 12 cores per node,
192GB RAM per node.
Customer has 2 cores from each
node = 4 cores
Customers
1,2….X
Customer “x”
Anti Money
Laundering /
Fraud
3 Storage Cells:
Raw Capacity:
21.6TB (HP)
108TB (HC)
Customer uses 500 GB
Strategic use of partitioning and
hybrid columnar compression.
Data extract selections are made
faster by use of the Exadata
storage indexes.
35
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
CUSTOMER EXAMPLE TWO: MARKETING AUTOMATION
BEFORE
CURRENT
CURRENT ENVIRONMENT
Partial - ¼ Exadata X2-2
Each ¼ has:
2 db nodes / 12 cores per node,
192GB RAM per node.
Customer 2 x 6
cores of Linux
Customer uses
2850 GB
•
•
Instant ETL updates with ZERO downtime by utilizing
partitioning for background processing and the
exchange partition function for promotion.
Saved much space by eliminating indexes that are no
longer required due to Exadata’s superior processing
power.
Customers
1,2….X
Customer uses 2 cores on each
node for total of 4 cores
Customer “x”
SAS
Marketing
Automation
3 Storage Cells:
Raw Capacity:
21.6TB (HP)
108TB (HC)
Customer uses 700GB
Used partitioning to run long ETL
and analytic jobs in the background
prior to daily promotion to
production.
36
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
CUSTOMER EXAMPLE THREE: FRAUD DETECTION
CURRENT ENVIRONMENT
BEFORE
Customer has 8
nodes of a
commercial
Postgres based
cluster. Each node
as 2x6 cores and
96 GB of RAM.
Customer uses
1800 GB per
database, 2
databases in place
at production level
per data center
•
•
CURRENT
1- ¼ RAC Exadata X3-2
Each ¼ RAC has:
2 db nodes / 12 cores per node,
256 GB RAM per node.
Customer has 6 cores from each
node = 12 cores
Customers
1,2….X
Customer “x”
Anti Money
Laundering /
Fraud
3 Storage Cells:
Raw Capacity:
21.6TB (HP)
108TB (HC)
Customer uses 600 GB per DB.
Daily ETL runs < 10 hours vs. > 20 hours
Interface in use by 33,000 users now returns all queries in less
than 30 seconds vs. many selections timing out at 3 minutes.
37
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS SOLUTIONS
PERFORMANCE IMPROVEMENT BENEFITS
ONDEMAND
•
•
•
•
Increased performance by removing indexes and letting the Exadata Storage Engine
do its work. Side benefit is more space for additional customers and databases
leading to an increased ROI.
Implemented an Information Lifecycle Management Policy to partition data where
possible and to compress data utilizing Hybrid Columnar Compression based on
usage and historic attributes.
Implemented Transparent Database Encryption as a standard for all customers.
• Very few other database vendors could compete against this option.
• Little performance impact as the data was encrypted in the DB Nodes BUT
decrypted by hardware at the storage nodes.
Utilized Instance Caging, Database Resource Management and IO Resource
Management to guarantee a level of performance to all customer.
38
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
Value of Quantified Benefits
THE BUSINESS DELIVERING IT & BUSINESS BENEFITS AT A LOWER COST
CASE FOR EXADATA OF OWNERSHIP
Business benefits result from Multitenancy, Agility and
improved IT performance:
 Superior services and processing
 Superior business intelligence
IT
Value-Add
IT Cost
Savings
Consolidation of:
 Storage
 Servers
 Data Center
 Labor
 SLAs
 Performance
 Speed
 Frequency
 Granularity
 Time-to-Market
Business
Benefits
 Increased Revenue
 Retention
 Growth
 Cost Management
 Direct Costs
 Expenses
 Asset Management
 Workforce
Productivity
39
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
ORACLE EXADATA BENEFITS FOR SAS END USERS
Better
performance
Better operational
support
Better scalability
–
• 40Gb/sec Infiniband interconnect between database and storage nodes and
externally to the SAS math tier
• Database aware Exadata Storage Server allow for the offload of data intensive
queries to the storage tier providing at least a ten-fold increase in query performance
• All support is handled by one team and one vendor. No longer necessary to call out
to multiple teams and try and get multiple vendors on the phone.
• We have streamlined the creation and delivery of new databases to the deployment
teams, with 12c we look forward to providing faster and more flexible options.
• Support more SAS users with the higher performance and I/O throughput provided
by Exadata
• Achieve linear scalability because of the capabilities of Exadata Storage Server
architecture
• Exadata has a balanced configuration designed to support SAS database loads
40
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
•
•
•
•
•
•
•
•
SAS SOLUTIONS
TECHNOLOGIES USED
ONDEMAND
Centralized management of all Oracle databases with Oracle Enterprise Manager 12c.
Utilized Oracle Advanced Security Option (ASO) for Transparent Database Encryption
with unique wallets/keys for each database.
Also utilized the ASO for SSL encryption of all client connections.
Utilized the Scan Listener to hand off to dedicated local listeners on their own port for
each database.
The compute tier for the solution had access to our Exadata DMZ only over the Scan
Listener port and the dedicated local listener port.
Backups are to ZFS and utilize mainly RMAN backup sets and opportunistic data
pump exports.
Database Partitioning and Hybrid Columnar Compression is used in our data lifecycle.
management strategy, we are still testing offloading image copies to ZFS.
Utilized Oracle Database Appliance as a Tier 2 database offering.
41
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS SOLUTIONS
LESSONS LEARNED
ONDEMAND
•
If you do not have RAC and GRID experience, then sign up for training as
soon as you place your order.
• Utilize Oracle’s onboarding services for Exadata if you are a first time buyer.
• Make sure you understand the performance implications between High
Performance Disks and High Capacity Disks in regards to your intended
usage.
• Investigate how data is being placed onto the disk, the default ASM
templates do not explicitly place any file types to the HOT area of the disk.
• If you are already a premium support customer, look into the platinum
support offerings available for Oracle Engineered Systems.
42
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS SOLUTIONS FUTURE DIRECTION
ONDEMAND
•
•
Evaluating Oracle Database 12c Multitenant
• Reduced TCO through the management of many
databases as one
• Lower resource utilization
• Lower administration costs
• Rapid cloning for development and debugging
• Tiered DBaaS offering
• Define Container Databases with different degrees
of availability – Single Instance, RAC, disaster
recovery with Data Guard
• Move customer’s pluggable database between tiers
with ease
Improved Information Lifecycle Management (ILM) with the
use of Automatic Data Optimization
43
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS SOLUTIONS
ONDEMAND
Questions?
44
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS SOLUTIONS CONTACT
ONDEMAND
Learn more about our services: http://www.sas.com/solutions/ondemand/index.html
Email: [email protected]
Blog: http://randywilcoxdba.wordpress.com/
45
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS EXADATA VALUE PROPOSITION
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
www.SAS.com
SAS on Oracle for Big Data and Cloud Services:
Insights into a Strong Partnership (CON8653)
Paul Kent, VP Big Data, SAS
Randy Wilcox, DBA Team Manager, SAS Solutions onDemand
Hermann Baer, Director Product Management, Oracle
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
EXTRA SLIDES
48
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS HIGH-PERFORMANCE ANALYTICS - CHOICE
Distributed
SMP (SAS 9.4)
•
•
•
Exalogic, BDA, OVCA
Oracle Linux
SPARC M5-32, Solaris 11.1
• Single domain test – 48 cores, 2TB RAM
• SMP – In-Memory Analytic Server (LASR)
•
•
Lift 100GB table from Exadata to LASR
-> “hp” PROCS running in multi-threaded fashion
Infiniband
49
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS Marketing Automation - Oracle SuperCluster
Optimized
Test environment at Oracle Solution Center




Oracle and SAS Institute jointly tested SAS
Marketing Automation with the Oracle
SPARC SuperCluster
Each of the SPARC T4-4 compute nodes
were partitioned into two domains, one
running Oracle Solaris 10 for SAS Marketing
Automation, and the other running Oracle
Solaris 11 and Oracle Database 11g
Oracle Exa Storage Cells accelerated the
Database performance
Infiniband network maximized I/O
throughput between nodes
50
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
51
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
SAS Marketing Automation on Oracle
SuperCluster - Comparison Results
OPN Partner and Oracle Internal and Confidential
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
52
Field Collateral
 Empowering SAS Grid
Computing and SAS Marketing
Automation on Oracle
SuperCluster (Presentation)
 Improving SAS Customer
Intelligence Solution
Performance with Oracle
SuperCluster (Paper)
OPN Partner and Oracle Internal and Confidential
C op yr i g h t © 2 0 1 3 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
53

similar documents