analytics in big data era

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ANALYTICS IN BIG DATA ERAANALYTICS TECHNOLOGY AND ARCHITECTURE TO MANAGE VELOCITY AND VARIETY,

DISCOVER RELATIONSHIPS AND CLASSIFY HUGE AMOUNT OF DATA

MAURIZIO SALUSTI SAS


AGENDA

From DBMS to BIG DATA

Big Data Analytics

Architectural Considerations

Methods

Data Discovery: Visual Analytics


The ability to generate, communicate, share, and access information has been revolutionized by the increasing number of people, devices, and sensors that are now connected by digital networks.

• People leave information in networks• Devices many ways to provide information• Data are a stream continuos of information • Data are not only measures but text, images, sounds

WHAT IS BIG DATA?DATA are everywhere:• IT organization often collect many data in EDW but them

need to integrate with many other sources


Spreading information need drastic changements into paradigm how companies collect their data and how they use it:

• Customer data are not only in Customer company DB. These data give partial customers vision: i.e. Telco operators collect customer voice and sms traffic, while many their customers establish contacts using social media and apps.

• Customers can give many signal on market preferences like a sensor on market but the actual data storage structures and their analytics tools are not be able to deal with these data.

ACTUAL COMPANY DATA ORGANIZATIONDATA ARE DEPLOYED INFORMATION AS SNAPSHOTS:• DATA WAREHOUSE• ANALYTICAL DATAMARTSSame information are replicated in several data structures provide slow updating process and slow renewal data.


“Big data” refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.The ability to store, aggregate, and combine data and then use the results to perform analysis in motion has become ever more accessible as trends.

TREND COMPANY DATA ORGANIZATIONNEEDS:• TO AVOID DATA PROLIFERATION• TO PROVIDE SEVERAL SCENARIO OF SAME DATA• DATA ENRICHMENT WITH SEVERAL SOURCES• QUICKLY DATA RENEWAL• TO PROVIDE PATTERN OF CHANGEMENTS SCENARIO


New ways to manage distributed and not structured in classical way data are needed:We need different paradigm to organize data and, above all, to query them.Collect several sources and manage them open several new problems:

• Relational data (GRAPH DATA) can be useful to understand event spreading in a population.

• Data in motion coming from several tools on field (sensor devices, smarthphone) provide dynamic pattern often without an history of their form

• Not always data are in structured data model• Often we need to join data with not same keys• Often data coming with periodic flow near real time• Often we need to recognize pattern from data changing

frequently

NEW QUESTIONS


• SQL Queries often are useless to reach these data:• Information are not organized into DB structures • Data are very different way to provides information:

i.e. text are not easy to query using traditional query languages.

• Merging are driven by fuzzy keys where you can assign group information according statistic relationship.

• Event can be happen driven from relational with other data rather from specific behavior.

ANALYSIS• Not always you can apply sampling to extract data• Not always you can join data to define ABT• Often you need to know how environment can influence

event: like buy, choice, changement. • Often we need to merging information collected with

different scope.


BIG DATA

What types?

• includes clickstream and interaction data from social media such as Facebook, Twitter, LinkedIn and blogs

Machine-to-machine data

• includes readings from sensors, meters, and other devices as part of the so-called “Internet of things.”

Big transaction data

• includes healthcare claims, telecommunications call detail records (CDRs), and utility billing records that are increasingly available in semi-structured and unstructured formats.

Biometric data

• includes fingerprints, genetics, handwriting, retinal scans, and similar types of data.

Human-generated data

• includes vast quantities of unstructured and semi-structured data such as call center agents’ notes, voice recordings, email, paper documents, surveys, and electronic medical records.


AGENDA


Big Data Analytics


Methods



Data are stored in different place and you have to know relationship MAPPING coming from different sources.

Here before you extract data your query have to know from which place into the net you have data.

DBMS and Datamart help to analyzing data coming from one central point data.

You need only to know where data is and their meaning.

Query are managed directly from DBMS


MULTI POINT DATA HUB BUILDING BLOCKS OF A BIG DATA ANALYTICS PROCESS

ANALYTICS


REFERENCE ARCHITECTURE EXAMPLE SAS-RACK IMPLEMENTATION

TERADATA

CLIENT

ORACLE

HADOOP

GREENPLUM


Input OutputHadoop

Metadata

High Performance

Analytics

VisualAnalytics


Input OutputIn memoryGRID COMPUTINGIn Database

VisualAnalytics

Metadata

High PerformanceAnalytics

Analytical Tool


AGENDA


Big Data Analytics


Methods



• Worrying about software performance is not a new concept at SAS

• What is New? Dedicated high-performance software Accelerated development

• Why Now?» Customer needs» Blade systems have proven viable platforms for high-performance

computing» New computing paradigms» Partnerships with MPP database vendors

SAS® HIGH-PERFORMANCE

ANALYTICS


SAS PROCEDURES

Single-threaded Multi-threaded

Not aware of distributed Aware of distributed computing environment computing environment Runs on client

Runs on client or DBMS appliance

proc logistic data=TD.mydata; class A B C; model y(event=‘1’) = A B B*C;run;

proc hplogistic data=TD.mydata; class A B C; model y(event=‘1’) = A B B*C;run;

THEN AND NOW


Disks – “/filesys”

Temp/Utility files to support SAS SAS Datasets

OPERATING SYSTEM

Process

SAS Process

(6) As execution continues, temporary data is written out to utility files on disk

*SMP HP PROCS do not load the entire source dataset into RAM – the SAS Process utilizes the MEMSIZE option as a boundary. No different than MVA or “regular” procs, datastep, etc.

13

2

46

5

libname disk BASE “/filesys”;proc hpreg data=disk.source; analytic stuff…run;

SAS Process Steps:(1) SAS Process Starts on HW & O/S(2) SAS sets up access library to disk(3) SAS starts HPREG PROC(4) HPREG reads data through ACCESS

during computation*(5) Multiple threads are launched to

process the incoming data

HP PROCS IN SINGLE SERVER


OPERATING SYSTEM

Process

SAS Process

(6) Processing occurs in parallel against in memory data

13

2

libname a sashdat;option set=gridhost=“NAMENODE”;proc hpreg data=a.source; analytic stuff… performance nodes=all;run;SAS Process Steps:(1) SAS Process Starts on HW & O/S(2) SAS sets up access library to disk(3) SAS starts HPREG PROC(4) Due to GRIDHOST and proper access

engine setting, multi-threaded processes are started on grid nodes (via TKGrid)(5) As TKGrid processes start up, ALL data is lifted into RAM from HDFS.

HPPROCS IN DISTRIBUTED ARCHITECTUREHADOOP HDAT – SHARED-RACK EXAMPLE

(7) Results return to initiating process on SAS Server

NODE 1

Data4 5

NODE 2

Data4 5

NODE N

Data4 5

6

6

6

7

HADOOP NAMENODE

4

4


Big data analysis can be done using several analytic strategy.

• SAS collects many different methods many of them coming from traditional statistical inference analysis using SEMMA paradigm.

• Other coming from stochastic process analysis both for continue and discrete events.

• Other coming from linear and not linear mixed models.

• Graph analysis


AGENDA


Big Data Analytics


Methods



Text Mining

• Parsing large-scale text collections

• Extract entities

• Auto. Stemming & synonym detection

Data Mining

• Complex

relationships• Tree-based

Classification• Variable

Selection

Optimization

• Local search optimization

• Large-scale linear & mixed integer problems

• Graph theory

Econometrics

• Probability of events

• Severity of random events

ANALYTICAL CATEGORIES AND TARGET USAGE

Forecasting

• Large-scale, multiple hierarchy problems

Statistics

• Binary target & continuous no. predictions

• Linear, Non-Linear, & Mixed Linear modeling


Data coming from different sources can be tie using different methods like canonical decomposition.

Data pattern variability on data in motion like data coming from devices can be sampled or simulate pattern distribution using Markov chain Monte Carlo methods .

Sparse vector data with missing values can be simulate using MCMC or other regression methods

Discrete choice among different events can be defined using multinomial discrete models.


Network

Community

The Network Analysis objectives are:

Identifying the subnets (communities) with high potential of information exchange.

Measuring changes over time.

Producing initiatives which increase the enterprise presence in the single communities knowing the spreading strength of the community.

GRAPH ANALYSIS


GRAPH ANALYSIS

A network is collection of the relationships among nodes by links.

A node is an individual featured by qualities which can be transmitted through the links (impulses).

A link is the relationship which connects 2 nodes. It can be outgoing, incoming or with no direction.

Node

Link

0 1 234

5 67

89

10 11

12 1314

15 16


AGENDA


Big Data Analytics


Methods



. . .provide very easy to use - yet sophisticated – statistical graphic tools to all of your users?

… use ad hoc exploration and visualizations to analyze multivariate results?

……quickly produce mobile dashboards and reports that convey more foresight than hindsight?

SAS® VISUAL ANALYTICS

A Single solution for Statistical

Visualization and reporting


SAS® VISUAL ANALYTICS BUSINESS VISUALIZATION DRIVEN BY ANALYTICS

EXPLORATION AND VISUALIZATION

POWER OF ANALYTICS

RAPID DELIVERY OF MOBILE INSIGHTS


BUSINESS VISUALIZATION

THE DIFFERENCE BETWEEN RAPID INSIGHT AND FAST INFORMATION

DATA VISUALIZATION ANALYTIC VISUALIZATION

EXPLORATION DISCOVERY


BENEFITS INCREASE THE USE OF ANALYTICS AND BI

• Self-service• Easy to use Analytics• Work with more data

• Reporting and Dashboards• Mobile BI• Collaboration


SAS® VISUAL ANALYTICS MEETING YOUR BUSINESS NEEDS THROUGH FLEXIBILITY

Traditional“on premise”Deployments

PublicPrivateHybrid

SAS Cloud&

SAS Solutions on Demand

analytics in big data era

Documents

data structures

customer data

relational data graph

data proliferationto

keysoften data

slow renewal data

structured data modeloften

sourcesquickly data