sas on oracle for big data and cloud services: insights into a

Copyr igh t © 2013 , SAS Ins t i tu te Inc . A l l r i gh ts reserved .

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

2 Copyr igh t © 2013 , SAS Ins t i tu te Inc . A l l r i gh ts reserved .

This session discusses how SAS’s high-performance analytics solutions running on Oracle engineered systems are tackling today’s big data problems by utilizing in-database and in-memory processing. The benefits of this collaborative effort enable joint customers to realize tangible value by analyzing all their data and reducing time to insight. SAS can score models against millions of records in seconds, using in-database processing on Oracle Database instances. Leveraging Oracle Exadata together with Oracle Exalogic, Oracle Big Data Appliance or Oracle Virtual Compute Appliance provides a platform for in-memory analytics, enabling analyses of much larger data sets with more-complex techniques over all of the company’s data.

“SAS and Oracle: Better Together.”


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


§  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


ROADMAP STEPS TO IMPROVING ANALYTICAL LIFECYCLE.

•  Projects based approach to building out standardised ADW platform & skills

•  Business issues must have tangible business value

•  Oracle/SAS can help with business case •  Pilot projects rolled out in 3-4 months •  Dedicated support from SAS/Oracle through

project lifecycle

Identify business

Issue

Map to standardised deployment

Build pilot project

Deliver initial value

assessment

Implement


BIG DATA When volume, velocity and variety of data exceeds an organization’s storage or compute capacity for accurate and timely decision-making

BIG ANALYTICS

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

ANALYTICS

The combination of using ANALYTICS on BIG DATA AND/OR the capability to run advanced or complex analytics on any size data.

OUR PERSPECTIVE Big Data is RELATIVE not ABSOLUTE


SAS® HIGH-PERFORMANCE

ANALYTICS KEY COMPONENTS


Oracle Engineered Systems I HARDWARE AND SOFTWARE

Exadata Database Machine

Exalogic Elastic Cloud

Oracle Virtualized Compute

Appliance (OVCA)

Big Data Appliance

SPARC SuperCluster

RDBMS storage compression and database parallelization via “Exadata Storage Servers”

Extreme -performance I/O connecting large amount of compute power and memory

VM Server virtualization – runs Oracle Linux, Oracle Solaris, Windows. Software Defined Networking

Massive disk storage array with high-bandwidth I/O for loading ‘big’ data

SPARC servers, high-performance I/O and Exadata storage servers in one rack


ANALYTICAL WORKLOAD

HOW ANALYTICAL LEADERS ARCHITECT TO EXPLOIT DATA

Analytical Services

Raw Data Pool (HDFS / NoSQL)

Tx Data Sources

Analytical Models and Rules Repository

Fast insights - IN-MEMORY

Event Management Platform

Event Data Store

Analytics Platform

Analytical Data Warehouse

Operational Execution

Event Data Store (RDBMS)

Event Stream Processing

R/T Decision Services

EDW

Event Streams

Analytical Visualization

ANALYTICS Inc. Enterprise Miner Discovery


ANALYTICAL WORKLOAD

Event Processing R/T Decisión Services

Analytical Services

Analytical Visualization


Fast insights - IN-MEMORY

ANALYTICS Inc. Enterprise Miner

Event Data Store (RDBMS)

Raw Data Pool

•  Bulk loading and management of data

•  Hadoop and No SQL stores •  Analysis performed in either Hadoop

or Event Data Store

Event Data Store

•  Relational data store for Tx detail •  Compressed for economic retention •  Common fabric between Engineered

Systems removes IO bottlenecks

Event Management Platform

•  Connecting insight to action through deployment of analytical models

•  Freely mix declarative, human workflow and non-deterministic rules

•  Stream data and events into Raw Data Pool and Event Data Store

Next Best Activity •  Process optimisation through R/T

decision engine

Model and rules repository

•  Model Factory - industrialising the deployment of analytics

In Memory

•  BIG MATH

Visualization and Analytics

•  Train of thought analysis •  Visual and network relationships •  Self Service (NO-OLAP DB) Discovery •  Guided and faceted discovery

Raw Data Pool (HDFS / NoSQL)

Discovery

HOW ANALYTICAL LEADERS ARCHITECT TO EXPLOIT DATA

Copyright © 2012, SAS Institute Inc. All rights reserved.

Analytic Data Warehouse / Marts

Relational Data Store

SAS Analyst’s Desktops

SAS Web Clients

SAS Metadata Server

SAS Compute Server

Web Application Server

SAS BUSINESS ANALYTICS FRAMEWORK

Server Tier Web Tier Client Tier Metadata Tier Data Tier


ANALYTICAL WORKLOAD

Analytical Services (on Oracle Exalogic / Big Data / OVCA )


SAS ANALYTICS Inc. Enterprise Miner

(HDFS / NoSQL)

Oracle Event Processing

Oracle Business

Rules

Oracle Policy Automation Real-Time Decisions

Database & options

Exadata Big Data Appliance

Big Data Connector

RAPID TIME TO VALUE IN STANDARD DEPLOYMENT

SAS Visual Analytics

SAS Business Rules

manager

SAS Event Stream

processing SAS Enterprise Decision

Management

SAS Visual Statistics

SAS High Performance

Analytics

SAS Analytics

Accelerator

SAS Grid-in-a-Box

SAS





SAS Web Clients

SAS Metadata Server

SAS Compute Server



Server Tier

Web Tier

Client Tier Metadata Tier Data Tier

Infiniband





SAS Web Clients

SAS Metadata Server

SAS Compute Server



Server Tier Web Tier

Client Tier Metadata Tier Data Tier

Infiniband


HOW DOES IT GET SUCH GOOD PERFORMANCE? SO….


HOW DOES IT WORK EXALOGIC/BDA/OVCA (COMPUTE) WITH EXADATA (STORAGE)

Exadata

Client

Exalogic / BDA / OVCA


HIGH-PERFORMANCE ANALYTICS

•  Using Different Data and Computing Appliances with Asymmetric HPA • 

SAS Server

Data Appliance (Exadata)

Controller Workers

Access Engine

Computing Appliance (Exalogic/BDA/OVCA)

TK

TKGrid

TK TK libname a oracle server=“dataAppliance”; proc hpcorr data=a.flights; performance

mode=asym host=“computingAppliance”; run;

General Captains


HIGH-PERFORMANCE ANALYTICS

SAS Server

Data Appliance (Exadata)

Controller Workers

Access Engine

Computing Appliance (Exalogic/BDA/OVCA)

TK

TKGrid

TK TK libname a oracle server=“dataAppliance”; proc hpcorr data=a.flights; performance

mode=asym host=“computingAppliance”; run;

General Captains

•  Using Different Data and Computing Appliances with Asymmetric HPA • 


Table 1: Summation of 5/20/100/200 columns; Baseline: DOP=1 (no parallelism) 120M rows, 400 columns, reg_simtbl_400

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 2: Scan times for 2 tables (200 columns, 400 columns, 120M rows); Baseline: SAS/ACCESS vs. HPA EP feeder !

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!)


SAS HIGH PERFORMANCE ANALYTICS, SAS VISUAL ANALYTICS ON ORACLE ENGINEERED SYSTEMS

bda101

bda102

bda103-bda118

Hadoop Datanode

SAS High-Performance

Analytics Server Root Node

SAS Visual Analytics Server

Tier SAS Visual

Analytics Middle Tier

SAS LASR In-Memory Analytics

Server

Big Data Appliance (BDA)

Hadoop Namenode


SAS Web Clients

Hadoop Datanode


Requirements Analysis

Platform Readiness

Data Acquisition

Model and Analyse

Deployment / Monitor Expand

SAS AND ORACLE

WORKING TOGETHER TO CREATE CUSTOMER VALUE

•  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

•  Template physical architectures developed based on use-cases

•  Physically tested and benchmarked together

•  Reduction in physical effort •  Overall reduction in lifecycle

costs

•  Best Practice papers •  SAS and Oracle Engineers

provide joint "Sizing and Architecture Analysis and Design"

Analysis Platform Analytics 3.0 Lifecycle Management


SAS AND ORACLE BETTER TOGETHER


SAS® EXADATA VALUE PROPOSITION Randy Wilcox, DBA Team Manager, SAS Solutions onDemand


SAS SOLUTIONS ONDEMAND OVERVIEW

•  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 •  Retail, financial services, health care, pharmaceutical, government, entertainment analytics •  Marketing and fraud analytic solutions

•  Experience supporting customers with unique situations •  Regulatory constraints - AML, FDA, HIPAA, Safe Harbor, SOC 2 / SOC 3 •  Working with multiple parties

•  Best Practices •  Innovative techniques •  Documented processes and procedures


SAS SOLUTIONS ONDEMAND ADVANCED ANALYTICS LAB

•  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


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


SAS SOLUTIONS ONDEMAND EXADATA AT SAS SOLUTIONS ON DEMAND

Business Objectives •  Maximize Investment – multitenant

DW/BI •  Consolidation of servers

•  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

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 Benefits Multitenant Agility

•  Deployed quarter racks in multiple data centers

•  Utilized ZFS Storage Appliance for a backup solution


SAS SOLUTIONS ONDEMAND CHALLENGES

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


SAS SOLUTIONS ONDEMAND EXADATA KEY REQUIREMENTS

31

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

Business Continuity: •  High availability SLA’s >99% •  Superior backup, restore, and recovery

•  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


SAS SOLUTIONS ONDEMAND MULTITENANT BENEFITS

Exadata X2-2 DB

Consolidation

Data Guard

Data Guard

•  Production •  Disaster Protection

Many Disparate Customer Systems

PROBLEMS: Typical usage <20% Costly Inefficient

BEFORE Current 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

Consolidated on Exadata

•  Test and QA


SAS SOLUTIONS ON DEMAND AGILITY BENEFIT

Single DBA team

HW OS provisioning Network/Firewall/VLAN configuration

Set and Deploy all FS for each DB

Manage Netbackup infra & all DB backups

IT Team

Network Team

Storage Team

Backup Team


SAS SOLUTIONS ON DEMAND AGILITY BENEFIT

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


CUSTOMER EXAMPLE ONE: ANTI-MONEY LAUNDERING

CURRENT 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

Customers 1,2….X

Customer “x” Anti Money Laundering / Fraud

CURRENT ENVIRONMENT 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 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.


CUSTOMER EXAMPLE TWO: MARKETING AUTOMATION

CURRENT BEFORE

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 “x” SAS Marketing Automation

CURRENT ENVIRONMENT Partial - ¼ Exadata X2-2 Each ¼ has: 2 db nodes / 12 cores per node, 192GB RAM per node. Customer uses 2 cores on each node for total of 4 cores 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.


CUSTOMER EXAMPLE THREE: FRAUD DETECTION

CURRENT 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

•  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.

Customers 1,2….X

Customer “x” Anti Money Laundering / Fraud

CURRENT ENVIRONMENT 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 3 Storage Cells: Raw Capacity: 21.6TB (HP) 108TB (HC) Customer uses 600 GB per DB.


SAS SOLUTIONS ONDEMAND PERFORMANCE IMPROVEMENT BENEFITS

•  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.


THE BUSINESS CASE FOR EXADATA

DELIVERING IT & BUSINESS BENEFITS AT A LOWER COST OF OWNERSHIP

IT Cost Savings

IT Value-Add

Business Benefits

§  Increased Revenue § Retention § Growth

§ Cost Management § Direct Costs § Expenses

§ Asset Management § Workforce

Productivity

§ SLAs § Performance

§ Speed § Frequency § Granularity

§ Time-to-Market

Valu

e of

Qua

ntifi

ed B

enef

its

Consolidation of: § Storage § Servers § Data Center § Labor

Business benefits result from Multitenancy, Agility and improved IT performance:

ü  Superior services and processing ü  Superior business intelligence


ORACLE EXADATA BENEFITS FOR SAS END USERS –

• 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

Better 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.

Better operational support

• 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

Better scalability


SAS SOLUTIONS ONDEMAND TECHNOLOGIES USED

•  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.


SAS SOLUTIONS ONDEMAND LESSONS LEARNED

•  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.


SAS SOLUTIONS ONDEMAND

FUTURE DIRECTION

•  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



Questions?



CONTACT

Learn more about our services: http://www.sas.com/solutions/ondemand/index.html Email: [email protected] Blog: http://randywilcoxdba.wordpress.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


EXTRA SLIDES


Distributed •  Exalogic, BDA, OVCA •  Oracle Linux

SMP (SAS 9.4)

•  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

SAS HIGH-PERFORMANCE ANALYTICS - CHOICE

Infiniband


SAS Marketing Automation - Oracle SuperCluster Optimized Test environment at Oracle Solution Center

OPN Partner and Oracle Internal and Confidential

l  Oracle and SAS Institute jointly tested SAS Marketing Automation with the Oracle SPARC SuperCluster

l  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

l  Oracle Exa Storage Cells accelerated the Database performance

l  Infiniband network maximized I/O throughput between nodes


SAS Marketing Automation on Oracle SuperCluster - Comparison Results



Field Collateral

§  Empowering SAS Grid Computing and SAS Marketing Automation on Oracle SuperCluster (Presentation)

§  Improving SAS Customer Intelligence Solution Performance with Oracle SuperCluster (Paper)


sas on oracle for big data and cloud services: insights into a

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