ANALYTICS IN BIG DATA ERA
ANALYTICS TECHNOLOGY AND ARCHITECTURE TO MANAGE VELOCITY AND VARIETY, DISCOVER RELATIONSHIPS AND CLASSIFY HUGE AMOUNT OF DATA
AGENDA
From DBMS to BIG DATA
Big Data Analytics
Architectural Considerations
Methods
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 ORGANIZATION
DATA ARE DEPLOYED INFORMATION AS SNAPSHOTS:
• DATA WAREHOUSE
• ANALYTICAL DATAMARTS
Same 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 ORGANIZATION
NEEDS:
• TO AVOID DATA PROLIFERATION
• TO PROVIDE SEVERAL SCENARIO OF SAME DATA
• DATA ENRICHMENT WITH SEVERAL SOURCES
• QUICKLY DATA RENEWAL
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
• 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
AGENDA
From DBMS to BIG DATA
Big Data Analytics
Architectural Considerations
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
REFERENCE
ARCHITECTURE EXAMPLE SAS-RACK IMPLEMENTATION
TERADATA
CLIENT
ORACLE
HADOOP GREENPLUM
Input Hadoop Output Metadata High Performance Analytics Visual Analytics
Input In memory GRID Output COMPUTING
In Database Visual Analytics
Metadata High Performance Analytics Analytical Tool
AGENDA
From DBMS to BIG DATA
Big Data Analytics
Architectural Considerations
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;
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.
1 3 2 4 6 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
OPERATING SYSTEM
Process
SAS Process
(6) Processing occurs in parallel against in memory data 1 3 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 ARCHITECTURE
HADOOP HDAT – SHARED-RACK EXAMPLE
(7) Results return to initiating process on
NODE 1 Data 4 5 NODE 2 Data 4 5 NODE N Data 4 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.
AGENDA
From DBMS to BIG DATA
Big Data Analytics
Architectural Considerations
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
AGENDA
From DBMS to BIG DATA
Big Data Analytics
Architectural Considerations
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
BUSINESS
VISUALIZATION THE DIFFERENCE BETWEEN RAPID INSIGHT AND FAST INFORMATION
DATA VISUALIZATION ANALYTIC VISUALIZATION
BENEFITS INCREASE THE USE OF ANALYTICS AND BI
• Self-service
• Easy to use Analytics • Work with more data
• Reporting and Dashboards • Mobile BI
SAS® VISUAL
ANALYTICS MEETING YOUR BUSINESS NEEDS THROUGH FLEXIBILITY
Traditional “on premise” Deployments Public Private Hybrid SAS Cloud &
