ANALYTICS IN BIG DATA ERA

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1 ANALYTICS IN BIG DATA ERA ANALYTICS TECHNOLOGY AND ARCHITECTURE TO MANAGE VELOCITY AND VARIETY, DISCOVER RELATIONSHIPS AND CLASSIFY HUGE AMOUNT OF DATA MAURIZIO SALUSTI SAS Copyr i g ht 2012, SAS Ins titut e Inc. All rights res er ve d.

2 AGENDA From DBMS to BIG DATA Architectural Considerations Big Data Analytics Methods Data Discovery: Visual Analytics

3 WHAT IS BIG DATA? DATA are everywhere: IT organization often collect many data in EDW but them need to integrate with many other sources 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

4 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. 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.

5 TREND COMPANY DATA ORGANIZATION NEEDS: 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 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.

6 NEW QUESTIONS 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 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

7 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. 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.

8 BIG DATA What types?

9 AGENDA From DBMS to BIG DATA Architectural Considerations Big Data Analytics Methods Data Discovery: Visual Analytics

10 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 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.

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

12 REFERENCE ARCHITECTURE EXAMPLE SAS-RACK IMPLEMENTATION CLIENT GREENPLUM TERADATA ORACLE HADOOP

13 Input Hadoop Output Visual Analytics Metadata High Performance Analytics

14 Input In memory GRID COMPUTING In Database Output Visual Analytics Metadata Analytical Tool High Performance Analytics

15 AGENDA From DBMS to BIG DATA Architectural Considerations Big Data Analytics Methods Data Discovery: Visual Analytics

16 SAS HIGH- PERFORMANCE ANALYTICS 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

17 SAS PROCEDURES THEN AND NOW proc logistic data=td.mydata; proc hplogistic data=td.mydata; class A B C; class A B C; model y(event= 1 ) = A B B*C; model y(event= 1 ) = A B B*C; run; run; Single-threaded Not aware of distributed computing environment Runs on client Multi-threaded Aware of distributed computing environment Runs on client or DBMS appliance

18 HP PROCS IN SINGLE SERVER 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 (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. Process Temp/Utility files to support SAS OPERATING SYSTEM 6 1 SAS Process Disks /filesys 4 SAS Datasets

19 HPPROCS IN DISTRIBUTED ARCHITECTURE HADOOP HDAT SHARED-RACK EXAMPLE 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. (6) Processing occurs in parallel against in memory data (7) Results return to initiating process on SAS Server OPERATING SYSTEM Process SAS Process HADOOP NAMENODE 4 NODE Data 6 NODE Data 6 NODE N 4 5 Data 6

20 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

21 AGENDA From DBMS to BIG DATA Architectural Considerations Big Data Analytics Methods Data Discovery: Visual Analytics

22 ANALYTICAL CATEGORIES AND TARGET USAGE Statistics Data Mining Text Mining Forecasting Econometrics Optimization Binary target & continuous no. predictions Linear, Non- Linear, & Mixed Linear modeling Complex relationships Tree-based Classification Variable Selection Parsing large-scale text collections Extract entities Auto. Stemming & synonym detection Large-scale, multiple hierarchy problems Probability of events Severity of random events Local search optimization Large-scale linear & mixed integer problems Graph theory

23 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.

24 GRAPH ANALYSIS Network The Network Analysis objectives are: Identifying the subnets (communities) with high potential of information exchange. Community Measuring changes over time. Producing initiatives which increase the enterprise presence in the single communities knowing the spreading strength of the community.

25 GRAPH ANALYSIS Node 2 Link 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.

26 AGENDA From DBMS to BIG DATA Architectural Considerations Big Data Analytics Methods Data Discovery: Visual Analytics

27 ...provide very easy to use - yet sophisticated statistical graphic tools to all of your users? SAS VISUAL ANALYTICS A Single solution for Statistical Visualization and reporting use ad hoc exploration and visualizations to analyze multivariate results? quickly produce mobile dashboards and reports that convey more foresight than hindsight?

28 SAS VISUAL ANALYTICS BUSINESS VISUALIZATION DRIVEN BY ANALYTICS EXPLORATION AND VISUALIZATION POWER OF ANALYTICS RAPID DELIVERY OF MOBILE INSIGHTS

29 BUSINESS VISUALIZATION THE DIFFERENCE BETWEEN RAPID INSIGHT AND FAST INFORMATION DATA VISUALIZATION ANALYTIC VISUALIZATION EXPLORATION DISCOVERY

30 BENEFITS INCREASE THE USE OF ANALYTICS AND BI Self-service Easy to use Analytics Work with more data Reporting and Dashboards Mobile BI Collaboration

31 SAS VISUAL ANALYTICS MEETING YOUR BUSINESS NEEDS THROUGH FLEXIBILITY Traditional on premise Deployments Public Private Hybrid SAS Cloud & SAS Solutions on Demand

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