Introducing Teradata Aster Discovery Platform Getting Started Ahsan Nabi Khan September 25 th, 2015

2 When You Need Aster Discovery Platform? 2.DIG DEEP AND FAST: Ad-hoc, interactive exploration of all data within seconds/minutes 1.SCALABLE ANALYTICS: Vast array of analytic algorithms run on commodity hardware as an Integrated Analytics Engine

3 Advanced Analytic Applications: Use Cases Credit, Risk and Fraud Packaging and Advertising Buying Patterns Cyber Defense Fraud and Crime Citizen’s Feedback Call Data Records Service Personalization Friends Graphs Click Stream Opinion, Sentiment, Stars Social Media Telecom Commerce Analysis Federal Analysis

Discovery Process Model

5 Hardware requirements: -6-8 GB memory -20 GB free disk space OS requirement: -64-bit XP, Vista, Win 7, and later Prereq. Software: -7-zip -VMware player Download Aster Discovery Platform from: - Downloading Aster 6.0

6 Installing Aster 6.0 Extract Aster 7-zip file. It contains folders for -Aster Queen VM -Aster Worker VM -Documentation Set IP address for VMNet8 Adapter to

7 Aster Management Console Start Queen and Worker VM on VM Player Point your browser to Username: db_superuser Password: db_superuser On Admin Panel, in Cluster Management, click on Activate Cluster

8 SQL-MR through ACT Use putty.exe to login into Aster Queen node shell -Queen IP address: Username: root (or) aster -Password:aster Access Aster Command Terminal (ACT) -Username: act -Password: beehive Create Tables, Query in SQL-MR

9 Setting Up Aster Lens for Visualization In ACT, put command -\dF Check that CfilterViz.zip and NpathViz.zip functions are installed alongwith prerequisite functions cfilter.zip and npath.viz If not, copy them from Analytic libraries in Aster Queen home folder using FileZilla Run command -\i NpathViz.zip -\i CfilterViz.zip Login Queen root using putty.exe Locate /opt/AsterLens/asterlens Start asterlens by running shell command -./start-asterlens.sh Point browser to Login AsterLens -Username:admin -Password:admin

10 Visualization through AsterLens Create Table Load Data through ncluster_loader Apply Cfilter to find association between Calling_Number and Called_Number Create AsterLens CfilterViz table from Cfilter result Add in AsterLens Catalog Create Table CDR ( Calling_Number VARCHAR(255), Called_Number VARCHAR(255), Minutes REAL ) DISTRIBUTE BY HASH(Calling_Number); ncluster_loader --hostname username beehive -- password beehive --dbname beehive --verbose --skip-rows 1 CDR 26_02_2015.csv –c SELECT * FROM Cfilter( ON (SELECT 1) PARTITION BY 1 PASSWORD('beehive') INPUTTABLE('CDR') OUTPUTTABLE('cdr_26_02_2015') INPUTCOLUMNS(‘calling_number',‘called_number') JOINCOLUMNS(‘calling_number') MAXSET(25) ); create table Aster_Lens.CDR_cfilterviz distribute by hash(col1_item1) as ( select * from CfilterViz( on cdr_26_02_2015 partition by 1 title(' calls') item1_col('col1_item1') item2_col('col2_item2') cnt1_col('cnt1') cnt2_col('cnt2') DIRECTED('true') score_col('cntb') accumulate('col1_item1', 'col2_item2') ) ); insert into Aster_Lens_Catalog values(1, 'aster_lens', ‘CDR_cfilterviz', '26_02_2015 calls');

11 Resulting AsterLens Visualizations

12 Example: k-Means Function What this gives you: -Organizes data into groupings or clusters based on shared attributes -Allows you to understand natural segments Example use cases: -Marketing segmentation -Fraud detection -Computer vision-- object recognition One call for clustering items into natural segments Complete Aster Data Application: Text processing required to prepare data for customer support analysis K-Means identifies hot product issues for proactive response K-Means in Use: Contact Center

13 Example: Basket Generator Function What this gives you? -Creates groupings of related items via single pass over data -Allows you to increase or decrease basket size with a single parameter change Example use cases: -Retail market basket analysis -People who bought x also bought y Extensible market basket analysis Complete Aster Data Application: Evaluate effectiveness of marketing programs Launch customer recommendations feature Evaluate and improve product placement Basket Generator in Use

14 Example: Unpack Function What this gives you: -Translates unstructured data from a single field into multiple structured columns -Allows business analysts access to data with standard SQL queries Example use cases: -Sales data -Stock transaction logs -Gaming play logs Transforming hidden data into analyst accessible columns Complete Aster Data Application: Text processing required to transform/unpack third party sales data Sessionization required to prepare data for path analysis Statistical analysis of pricing Unpack in Use: Pricing Analysis

15 Example: nPath Function for time-series analysis What this gives you: - Pattern detection via single pass over data -Allows you to understand any trend that needs to be analyzed over a continuous period of time Example use cases: - Web analytics– clickstream, golden path - Telephone calling patterns - Stock market trading sequences Uncovering patterns in sequential steps Complete Aster Data Application: Sessionization required to prepare data for path analysis nPath identifies marketing touches that drove revenue nPath in Use: Marketing Attribution

16 Aster Analytics is much more diverse and powerful in finding “interesting” patterns while loses no information assimilated from simpler queries. Simple SQL queries cannot match the analytical power of Aster Analytics. For Big Data Analytics, Teradata and Aster create a powerful combination. Conclusion