1. Big Data Hands-On Labs:
Date
Time
Location
Tuesday
3:45pm – 4:45pm
Hotel Nikko - Peninsula
Wednesday
1:15pm – 2:15pm
Thursday
11:30am – 12:30pm
Or download: Big Data Lite Virtual Machine

2. Oracle Big Data Appliance for Customers and Partners
Jean-Pierre Dijcks
Oracle
Big Data Product Management
Paul Kent
SAS
VP Big Data

3. Oracle Big Data Appliance for Customers and Partners1
Big Data Appliance Recap
Why You Should Consider Big Data Appliance Driving Business Value with SAS on Big Data Appliance
Q&A 2 3 4

4. Oracle Big Data Management System
Oracle Big Data SQL
Oracle Database
Oracle Industry Models
Oracle Advanced Analytics
Oracle Spatial & Graph
Cloudera Hadoop
Oracle NoSQL Database
Oracle R Advanced Analytics for Hadoop
Oracle R Distribution
Oracle Database
Oracle Advanced Security
Oracle Advanced Analytics
Oracle Spatial & Graph
Oracle Big Data Connectors
Oracle Data Integrator
Big Data Appliance
Oracle Exadata
SOURCES

5. Recap: Big Data Appliance Overview
Big Data Appliance X4-2
Sun Oracle X4-2L Servers with per server:
2 * 8 Core Intel Xeon E5 Processors
64 GB Memory
48TB Disk space
Integrated Software:
Oracle Linux, Oracle Java VM
Oracle Big Data SQL*
Cloudera Distribution of Apache Hadoop – EDH Edition
Cloudera Manager
Oracle R Distribution
Oracle NoSQL Database
* Oracle Big Data SQL is separately licensed

6. Recap: Standard and Modular
Starter Rack is a fully cabled and configured for growth with 6 servers
In-Rack Expansion delivers 6 server modular expansion block
Full Rack delivers optimal blend of capacity and expansion options
Grow by adding rack – up to 18 racks without additional switches

7. Recap: Harness Rapid Evolution
BDA 4.0 – Sept 2014
Big Data SQL
Node Migration b
BDA 4.0
BDA 2.x – April 2013
Starter Rack
In-Rack Expansion
EM Integration
BDA 3.x – April 2014
CDH 5.0 (MR2 & YARN)
AAA Security
Encryption
BDA 1.0 – Jan 2012
Initial BDA
Mammoth Install

8. Core Design Principles for Big Data Appliance
Operational Simplicity
Simplify Access to ALL Data

9. Core Design Principles for Big Data Appliance
Oracle Big Data SQL
Oracle SQL on ALL your data
All Native Oracle SQL Operators
Smart Scan for Optimized Performance
Oracle Security
Govern all Data through a Single Set of Security Policies
Operational Simplicity
Simplify Access to ALL Data

10. Oracle Big Data SQL – A New Architecture
Powerful, high-performance SQL on Hadoop
Full Oracle SQL capabilities on Hadoop
SQL query processing local to Hadoop nodes
Simple data integration of Hadoop and Oracle Database
Single SQL point-of-entry to access all data
Scalable joins between Hadoop and RDBMS data
Optimized hardware
Balanced Configurations
No bottlenecks
Big Data SQL represents a new architecture for querying data in its natural format, wherever it leves, and – when running on Oracle Big Data Appliance and Oracle Exadata – provides a world-class Big Data Management System.
Oracle Confidential – Internal/Restricted/Highly Restricted

11. Big Data SQL 10’s of Gigabytes of Data Hadoop Cluster Oracle Database
SELECT w.sess_id, c.name
FROM web_logs w, customers c
WHERE w.source_country = ‘Brazil’
AND w.cust_id = c.customer_id;
Relevant SQL runs on BDA nodes
CUSTOMERS
WEB_LOGS
Big Data SQL
Only columns and rows needed to
answer query are returned
Big Data SQL’s Smart Scan capability radically reduces the cost of joining data with Oracle Database as well. When a join between massive data in Hadoop and smaller data in Oracle occurs, Big Data SQL can process rows using Bloom filters. This ensures that only data from Hadoop which meets the join conditions are transmitted back to the database.
As before, this can reduce the amount of data being transmitted and processed by the database by an order of magnitude or more. But in this case, Oracle Database is responsible for joining a part of average sized tables. By processing data at the source, whether it’s stored in Hadoop or Oracle Database, Big Data SQL ensures the best possible use of all the compute resources in a Big Data Management System.
10’s of Gigabytes of Data
Hadoop Cluster
Oracle Database

12. Big Data SQL SQL Push Down in Big Data SQL
SELECT w.sess_id, c.name
FROM web_logs w, customers c
WHERE w.source_country = ‘Brazil’
AND w.cust_id = c.customer_id;
SQL Push Down in Big Data SQL
Hadoop Scans on Unstructured Data
WHERE Clause Evaluation
Column Projection
Bloom Filters for Better Join Performance
JSON Parsing, Data Mining Model Evaluation
Relevant SQL runs on BDA nodes
CUSTOMERS
WEB_LOGS
Big Data SQL
Only columns and rows needed to
answer query are returned
Big Data SQL’s Smart Scan capability radically reduces the cost of joining data with Oracle Database as well. When a join between massive data in Hadoop and smaller data in Oracle occurs, Big Data SQL can process rows using Bloom filters. This ensures that only data from Hadoop which meets the join conditions are transmitted back to the database.
As before, this can reduce the amount of data being transmitted and processed by the database by an order of magnitude or more. But in this case, Oracle Database is responsible for joining a part of average sized tables. By processing data at the source, whether it’s stored in Hadoop or Oracle Database, Big Data SQL ensures the best possible use of all the compute resources in a Big Data Management System.
10’s of Gigabytes of Data
Hadoop Cluster
Oracle Database

13. Oracle Communications Data Model
Reference Architecture
Data Sources
Oracle Comms Apps (BSS/OSS)
Oracle Comms Ntwk Products (Tekelec & Acme)
Other Oracle Apps (CRM, ERP, etc.)
Third Party Sources
Data Management
Big Data Platform (Hadoop/NoSQL)
Relational Data Warehouse (OCDM)
ETL/ELT Adapters
Customer Experience
Real-Time Adapters
Operations
Third Party
Monetization
Adapters
Analytic Apps
Feedback Loop
To Other Apps

14. Core Design Principles for Big Data Appliance
Operational Simplicity
Simplify Access to ALL Data

15. Core Design Principles for Big Data Appliance
No Bottlenecks
Full Stack Install and Upgrades
Simplified Management
Cluster Growth
Critical Node Migration
Always Highly Available
Always Secure
Very Competitive Price Point
Operational Simplicity
Simplify Access to ALL Data

16. Successful Big Data Systems Grow
From Cluster Install with HA to Large Clusters to Dealing with Operational Issues
Day 1
12 node BDA for Production
Hadoop HA and Security Set-up
Ready to Load Data
Full install with a single command:
./mammoth –i rck_1
This is a small example using the Name Nodes (HA setup) as an example how things change (automatically) on a BDA, and how critical node migration is happening.
RCK_1

17. Successful Big Data Systems Grow
From Cluster Install with HA to Large Clusters to Dealing with Operational Issues
Day 1
This is a small example using the Name Nodes (HA setup) as an example how things change (automatically) on a BDA, and how critical node migration is happening.
RCK_1 N
Example Service: Hadoop Name Nodes

18. Successful Big Data Systems Grow
From Cluster Install with HA to Large Clusters to Dealing with Operational Issues
Day 90
Add 12 New Nodes across two Racks
Cluster expansion with a single command:
mammoth –e newhost1,…,newhostn
This is a small example using the Name Nodes (HA setup) as an example how things change (automatically) on a BDA, and how critical node migration is happening.
RCK_1
RCK_2 N N

19. Successful Big Data Systems Grow
From Cluster Install with HA to Large Clusters to Dealing with Operational Issues
Cluster Expansion with a single command:
mammoth –e newhost1,…,newhostn
This is a small example using the Name Nodes (HA setup) as an example how things change (automatically) on a BDA, and how critical node migration is happening.
RCK_1
RCK_2
This expansion automatically optimizes HA setup across multiple racks N
Because of uniform nodes and IB networking, no data is moved N

20. Successful Big Data Systems Grow
From Cluster Install with HA to Large Clusters to Dealing with Operational Issues
Day n
Critical Node Failure => Primary Name Node
This is a small example using the Name Nodes (HA setup) as an example how things change (automatically) on a BDA, and how critical node migration is happening.
RCK_1
RCK_2 N N

21. Successful Big Data Systems Grow
From Cluster Install with HA to Large Clusters to Dealing with Operational Issues
Automatic Failover to other NameNode
Automatic Service Request to Oracle for HW Failure
This is a small example using the Name Nodes (HA setup) as an example how things change (automatically) on a BDA, and how critical node migration is happening.
RCK_1
RCK_2 N N

22. Successful Big Data Systems Grow
From Cluster Install with HA to Large Clusters to Dealing with Operational Issues
Restore HA with a Single command
bdacli admin_cluster migrate N1
This is a small example using the Name Nodes (HA setup) as an example how things change (automatically) on a BDA, and how critical node migration is happening.
RCK_1
RCK_2
Reinstate the Repaired Node with a Single Command:
bdacli admin_cluster reprovision N1 N N

23. Core Design Principles for Big Data Appliance
30%
Quicker to Deploy
“Oracle Big Data Appliance is an excellent choice for customers looking to work with the full suite of Cloudera’s leading Hadoop-based technology. It’s more cost-effective and quicker to deploy than a DIY cluster.”
Operational Simplicity
21%
It’s logical that an engineered system would be quicker to deploy that building your own. It’s that first one that people don’t believe. But we worked with an analyst firm, ESG, and they measured list prices, did the comparison and the BDA is at least 20% cheaper than building your own comparable cluster, assuming that you have the time and skills to do so. The key word here is comparable – I’ve had lots of people tell me that they could build one cheaper, but they turn out to have a fraction of the storage. These are large, dense storage nodes, and if you use cheap pizza box servers, you can fill a rack much more cheaply, but you’ll have way less storage.
Cheaper to Buy
Mike Olson, Cloudera founder, Chief Strategy Officer, and Chairman of the Board

24. Big Data Initiative @ Oracle Global Support Services
Real-time access to better data means better insights, which means better decisions and better business results
Integrate data associated with customer telemetry, configurations, service history, diagnostics, knowledge & support information
Anticipate
Detect
Predict
Automate
Delight

25. Core Design Principles Enable Success
Operational Simplicity
Simplify Access to ALL Data

26. There is one more thing…
Business Value = Applications

27. Big Data Appliance powers instant Business Value
Customer Experience Management
Communications Data Model
Cyber Security Solutions

28. Introducing
Paul Kent - SAS

29. Big Data and Big Analytics – So Much more Gunpowder!
Paul Kent
VP BigData, SAS Research and Development

30. 1. Change Safari Pics

31. [CON8279] Oracle Big Data Appliance: Deep Dive and Roadmap for Customers and Partners
Oracle Big Data Appliance is the premier Hadoop appliance in the market. This session describes the roadmap for customers in the areas of high-performance SQL on Hadoop and securing big data, plus overall performance improvements for Hadoop. A special focus in the session is the roadmap and benefits Oracle Big Data Appliance brings to Oracle partners. To illustrate the benefits of running on a standardized and optimized Hadoop platform, SAS presents the findings of its tests of SAS In-Memory Analytics on Oracle Big Data Appliance.

32. Agenda SAS & Oracle Partnership Family Stories Deployment Patterns
Hadoop
Oracle Engineered Systems Family
SAS Software Family
Deployment Patterns

33. Reflection on a stronger partnership than ever
Both leaders in Big Data –
Jointly solving the most difficult and demanding Big Data Problems
Providing simplicity and agility to create flexible configurations
Extensive engineering collaboration
Can we answer:
How Does it Work?
How Does it Perform?
2014
#SASGF13
Copyright © 2013, SAS Institute Inc. All rights reserved.

34. SOURCE: http://commons.wikimedia.org/wiki/File:Tamoxifen-3D-vdW.png
the
tamoxifen
dilemma
SOURCE:

35. Agenda SAS & Oracle Partnership Family Stories Deployment Patterns
Hadoop
Oracle Engineered Systems Family
SAS Software Family
Deployment Patterns

37. Elephant :: 3 Good Ideas !! Never forgets Is a good (hard) worker
Is a Social Animal (teamwork)

38. Hadoop – Simplified View
Controller
Worker Nodes
MPP (Massively Parallel) hardware running database-like software
“data” is stored in parts, across multiple worker nodes
“work” operates in parallel ,on the different parts of the table

39. Idea #1 - HDFS. Never forgets!
Head Node
Data 1
Data 2
Data 3
Data 4…
MYFILE.TXT
..block1 ->
block1
..block2 ->
block2
..block3 ->
block3

40. Idea #1 - HDFS. Never forgets!
Head Node
Data 1
Data 2
Data 3
Data 4…
MYFILE.TXT
..block1 ->
block1
block1 copy2
..block2 ->
block2
block2 copy2
..block3 ->
block3 copy2
block3

41. Idea #1 - HDFS. Never forgets!
Head Node
Data 1
Data 2
Data 3
Data 4…
MYFILE.TXT
..block1 ->
block1
block1copy2
..block2 ->
block2
block2 copy2
..block3 ->
block3 copy2
block3 X X

42. Redundancy Wins!

43. Idea #2 – MapReduce – Send the work to the Data
We Want the Youngest Person in the Room
Each Row in the audience is a data node
I’ll be the coordinator
From outside to center, accumulate MIN
Sweep from back to front.
Youngest Advances

44. Agenda SAS & Oracle Partnership Family Stories Deployment Patterns
Hadoop
Oracle Engineered Systems Family
SAS Software Family
Deployment Patterns

45. Recap: Standard and Modular
Starter Rack is a fully cabled and configured for growth with 6 servers
In-Rack Expansion delivers 6 server modular expansion block
Full Rack delivers optimal blend of capacity and expansion options
Grow by adding rack – up to 18 racks without additional switches

46. Oracle Big Data SQL – A New Architecture
Powerful, high-performance SQL on Hadoop
Full Oracle SQL capabilities on Hadoop
SQL query processing local to Hadoop nodes
Simple data integration of Hadoop and Oracle Database
Single SQL point-of-entry to access all data
Scalable joins between Hadoop and RDBMS data
Optimized hardware
Balanced Configurations
No bottlenecks
Big Data SQL represents a new architecture for querying data in its natural format, wherever it leves, and – when running on Oracle Big Data Appliance and Oracle Exadata – provides a world-class Big Data Management System.
Oracle Confidential – Internal/Restricted/Highly Restricted

47. Diversity. It’s a good thing!
Impala
Nyala

48. Agenda SAS & Oracle Partnership Family Stories Deployment Patterns
Hadoop
Oracle Engineered Systems Family
SAS Software Family
Deployment Patterns

49. 4 Important Things
#1 Join the Family

50. SAS ACCESS to Hadoop
SAS
SERVER
HADOOP
Hive QL
#2 Be Familiar

51. SAS / Embedded Process SAS SERVER SAS/Scoring Accelerator for Hadoop
SAS Data Step
& DS2
SAS
SERVER
proc ds2 ; /* thread ~ eqiv to a mapper */ thread map_program; method run(); set dbmslib.intab; /* program statements */ end; endthread; run; /* program wrapper */ data hdf.data_reduced; dcl thread map_program map_pgm; method run(); set from map_pgm threads=N; /* reduce steps */ end; enddata; run; quit;
SAS/Scoring Accelerator for Hadoop
SAS/Code Accelerator for Hadoop
SAS/Data Quality Accelerator for Hadoop

52. SAS / High Performance Analytics
HADOOP
SAS HPA
Procedures
SAS
SERVER
#3 Use the Cluster!

53. SAS / High Performance Analytics
Prepare
Explore / Transform
Model
HPDS2
HPDMDB
HPSAMPLE
HPSUMMARY
HPCORR
HPREDUCE
HPIMPUTE
HPBIN
HPLOGISTIC
HPREG
HPNEURAL
HPNLIN
HPCOUNTREG
HPMIXED
HPSEVERITY
HPFOREST
HPSVM
HPDECIDE
HPQLIM
HPLSO
HPSPLIT
HPTMINE
HPTMSCORE
What HPA procs … this is the short list these run in Hadoop today…
HPDS2 – Parallel Execution of DS2
HPDMDB – Metadata definitions and data Summarization
HPSAMPLE - Sampling and data partitioning
HPSUMMARY – Summarization Descriptive Statistics
HPCORR - Pearson correlation coefficients, three nonparametric measures of association, and the probabilities associated with these statistics
HPREDUCE – unsupervised variable selection
HPIMPUTE – Missing value replacement
HPBIN – binning
HPLOGISTIC – Logistic regression
HPREG – regression
HPNEURAL – Neural networks
HPNLIN – Non linear regression
HPCOUNTREG – regression of count variables
HPMIXED – Mixed Linear models
HPFOREST – random forest
HPSVM – support vector machine
HPDECIDE – Decision / Cost
HPLSO – Lasso
HPTMINE – Text Mining
HPTSCORE – Text Scoring
HPREG linear regression and variable selection
HPLOGISTIC logistic regression and variable selection
HPLMIXED linear mixed models
HPNEURAL neural nets
HPNLIN nonlinear regression and maximum likelihood
HPREDUCE covariance/correlation analysis, variable reduction
PROC HPREG
High-performance combination of REG and GLMSELECT
Supports
»classical variable selection techniques
»modern variable sélection techniques (LAR, LASSO)
CLASS variables
GLM and reference parameterizations
SELECTION statement
PROC HPNLIN
High-performance combination of NLIN and NLP/NLMIXED
»Classical nonlinear least squares (Levenberg-Marquardt)
»Maximum likelihood for built-in distributions
»Maximum likelihood for general, user-specified obj. functions
»Boundaries, linear equality/inequality constraints

54. SAS / High Performance Analytics
Controller
Client
Some processes are more complex that fits “nicely” inside the terms & conditions of the container.
We can use the embedded process as a data acquisition channel, and yet perform the mathematics elsewhere (and in the first generation, elsewhere meant other operating system processes on the same server – preserving a symetric or 1:1 balance between the data parallelism and the mathematics parallelism)
2012 – SAS High Performance appliances for teradata, greenplum, oracle and hadoop

56. #1 Join the Family
#2 Be Familiar
#3 Use the cluster
#4 Have a pretty face!

57. SAS Visual Analytics Interactive exploration, dashboards and reporting
Auto-charting automatically picks the best graph
Forecasting, scenario analysis, Decision Trees and other analytic visualizations
Text analysis and content categorization
Feature-rich mobile apps for iPad® and Android

59. SAS Visual Statistics July-2014
Interactive, visual application for statistical modeling and classification
Multiple methods:
logistic, Regression, GLM, Trees, Forest, Clustering and more…
Model comparison and assessment
Group BY Processing

61. 4 Important Things (for cluster friendly software)
Join the Family
Be Familiar
Performance
Have a pretty face

62. Agenda SAS & Oracle Partnership Family Stories Deployment Patterns
Hadoop
Oracle Engineered Systems Family
SAS Software Family
Deployment Patterns

63. SAS Big Data on Big Data Appliance
Flexible Architectural options for SAS deployments
Can run on Starter, Half and Full configurations
Optionally select nodes “N, N-1, N-2, …” for additional SAS Services such as SAS Compute Tier, SAS MidTier
Optionally select node subset “N, N-1, N-2, N-3, …) for more dedicated resources for SAS Analytic Compute Environment by shifting Big Data Appliance roles
Option to selectively add more memory on a per node basis depending on specific workload distribution

64. STARTER BDA
SAS HPA Root Node
SAS Visual Analytics
Metadata Server
SAS Compute
SAS Midtier … …
SAS Visual Analytics, high-Performance Analytic Compute environment Co-Located with Hadoop

65. … … STARTER BDA Consider: Extra Memory for 5,6?
SAS HPA Root Node
SAS Visual Analytics
Metadata Server
SAS Compute
SAS Midtier
Consider:
Extra Memory for 5,6? … …
SAS Visual Analytics, high-Performance Analytic Compute environment Co-Located with Hadoop

66. FULL RACK BDA
SAS HPA Root Node
Metadata Server
SAS Compute
SAS Midtier
LASR Worker 18
HDFS Data 18
LASR Worker 17 … …
HDFS Data 17 …
SAS Visual Analytics, high-Performance Analytic Compute environment Co-Located with Hadoop

67. Assembled in OSC, SYDNEY AUSTRALIA
FULL RACK BDA
Assembled in OSC, SYDNEY AUSTRALIA

68. Assembled in OSC, SYDNEY AUSTRALIA
FULL RACK BDA
Assembled in OSC, SYDNEY AUSTRALIA

69. Assembled in OSC, SYDNEY AUSTRALIA
FULL RACK BDA
Assembled in OSC, SYDNEY AUSTRALIA

70. Assembled in OSC, SYDNEY AUSTRALIA
FULL RACK BDA
Assembled in OSC, SYDNEY AUSTRALIA
Basic Smoke Tests Confirmed:
Interoperate with Hadoop and Map Reduce
Read and Write text files to/from HDFS
Read and Write Tabular files to/from Hive (will confirm Oracle BIGSQL in OSC-SC)
Read and Write SAS binary format files to/from HDFS
High Degree Of Parallelism (DOP) reads via Map-Only jobs
SAS LASR server co-exists on/with datanodes
SAS HPA tasks scheduled on datanodes

71. SAS High-Performance Analytics Performance SAS Format Data (SASHDAT)
7171
SAS High-Performance Analytics Performance
SAS Format Data (SASHDAT)
1107 var
Mobs
97GB
5.7GB/node
Mobs
608GB
35.7GB/node 6x
Create
sec
sec 11
Scan/Count
24.60 sec
sec
10.5
HPCORR
295.20
4.7
HPCNTREG
336.79
4.6
HPREDUCE (u)
236.55
10.4
HPREDUCE (s)
219.50
9.3
Table 1: Summation of 5/20/100/200 columns;
Baseline: DOP=1 (no parallelism)
120M rows, 400 columns, reg_simtbl_400

72. OSC-AU FullRack BDA 408 Threads 600 GB dataset 17 servers
Your Problem solved ASAP

76. … … Exadata IntegraTion SAS Embedded Processing (EP) to Exadata
Leveraging Big Data SQL
SAS HPA Root Node
SAS Visual Analytics
Metadata Server
SAS Compute
SAS Midtier
LASR Worker 18 … …
HDFS Data 18
SAS EP
Big Data SQL

77. SAS High-Performance Analytics Performance
7777
SAS High-Performance Analytics Performance
SAS EP Parallel Data Feeders
DOP=1
DOP=24
(flash cache)
Add(5)
1.25min
1.5min
.5min
Add(20)
2.5min
Add(100)
13min
.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

78. SAS High-Performance Analytics Performance
7878
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

79. SAS High-Performance Analytics Performance
7979
SAS High-Performance Analytics Performance
SAS Format Data (SASHDAT) and Oracle EXADATA
1107 var
Mobs
97GB
5.7GB/node
SASHDAT
907 var
79.7GB
4.7GB/node
EXADATA
Mobs
608GB
35.7GB/node
Create
sec
sec
sec
Scan/Count
24.60 sec
sec
sec
HPCORR
295.20
833.24
HPCNTREG
336.79
756.97
HPREDUCE (u)
236.55
HPREDUCE (s)
219.50
Table 1: Summation of 5/20/100/200 columns;
Baseline: DOP=1 (no parallelism)
120M rows, 400 columns, reg_simtbl_400

80. Oracle Engineered Systems for
ExaData
ExaLogic
SuperCluster
Big Data
Appliance
ZFS
Storage
Appliance
Virtual
Compute
Appliance
Database
Backup, Recovery,
Logging Appliance

81. Working together to create customer value
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"

82. SAS and Oracle Paul.Kent @ sas.com @hornpolish paulmkent
Better Together

83. Oracle Confidential – Internal/Restricted/Highly Restricted