SAS, JMP and Rfor Big Data

D. Zeitler

April 13, 2016

Modern computer architecture

Analytics Components

Processors

Cache

Main memory

Graphics Processing Unit(GPU)

Disk drive(s)

Network interface

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3/20

Analytics platforms

Notebook/tablet

Desktop

Stand alone server

Server farm

Cloud

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4/20

Three different approachesSAS 9.4, JMP Pro 12, R 3.2.3

SAS 9.4

Pro'sCon's

Backward compatibility(extreme)

Enterprise level support

Respected and accepted

Big data on limited hardware(sort of)

Supports high end hardware

Academic credentials

Lots of experts available (fora price)

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Cost and slow adoption·

6/20

JMP Pro 12

ProCon

Highly visual

Advanced GUI

Very fast for most analyses

Cross platform(Win/Mac/Linux)

Good OO programmability

High integration with SAS

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Cost (maybe more than SAS)

In-memory operation limitssize

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7/20

R 3.2.3

Pro ConAdvanced languagecapabilities

Unlimited visualizationcapability

Low cost (even for enterpriseversions)

Quick adoption of newtechniques

Interactive environment

Global free supportcommunity

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8/20

Why not Python?

Even more 'roll your own' than R

I usually opt for C++ rather than Python

This is about statistical software

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Head to head marketing

Big Data Analytics | Benchmarking SAS®, R, and MahoutSource(http://support.sas.com/resources/papers/Benchmark_R_Mahout_SAS.pdf)

Revolution R Enterprise: Faster Than SASSource (http://info.revolutionanalytics.com/SAS-Benchmark-White-Paper.html) You'll need to give them your contact info tolook at it.

So we have the gist in the next two slides.

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These are the tasks.

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Here's the punchline.

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Handling Big data

General approaches

Sampling (row/cases & column/variables)

External data base and SQL

Big hardware

Chunking

External calls (Rcpp)

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SAS 9.4

Developed vintage 1960's onmainframes (Think punchcards and tapes)

Searched for 'When was SASfirst written'. Second entrywas 'When was the Biblewritten?'

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15/20

JMP Pro 12

Vintage 1980's on Mac

platform

GUI oriented

Highly graphics oriented

Expensive

Well supported and

integrated with SAS 9.4

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16/20

R/RStudio 3.2.3

Vintage 1980's on Unixsystems

Under constant development

Freeware

Commercialized versionsavailable

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17/20

SAS specific big data approaches

If you can afford it, SAS is probably the quickest way to solutions.

So pay the bucks for:Enterprise Miner

Visual Analytics

Factory Miner

Contextual Analysis

SAS/OR

Simulation Studio

– the list goes on… –

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JMP specific big data approaches

SAS JMP Pro (instead of the cheaper JMP offering)

SAS server

SAS code

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R specific big data approaches

Alternative interpreters (pqR

(http://radfordneal.github.io/pqR/), Renjin

(http://www.renjin.org/), TERR

(http://spotfire.tibco.com/en/discover-spotfire/what-does-

spotfire-do/predictive-analytics/tibco-enterprise-runtime-for-r-

terr.aspx), Oracle R

(http://www.oracle.com/technetwork/indexes/downloads/r-

distribution-1532464.html) )

Tessera (http://tessera.io)

Microsoft R Server - XDF files (https://www.microsoft.com/en-

us/server-cloud/products/r-server/)

Spark/R (https://spark.apache.org/docs/latest/sparkr.html)

The "Programming with Big Data in R" project (pbdR)

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