Data Science and Visualization Discovery Platform Aster Analytics Getting Started Presentation

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

2. 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. 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

4. Discovery Process Model

5. 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:
- http://developer.teradata.com
Downloading Aster 6.0

6. 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 192.168.100.1

7. 7
Aster Management Console
• Start Queen and Worker VM on VM Player
• Point your browser to https://192.168.100.100
• Username: db_superuser
• Password: db_superuser
• On Admin Panel, in Cluster Management, click on Activate
Cluster

8. 8
SQL-MR through ACT
• Use putty.exe to login into Aster Queen node shell
- Queen IP address: 192.168.100.100
- Username: root (or) aster
- Password:aster
• Access Aster Command Terminal (ACT)
- Username: act
- Password: beehive
• Create Tables, Query in SQL-MR

9. 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
http://192.168.100.100:10
• Login AsterLens
- Username:admin
- Password:admin

10. 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 192.168.100.100 --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('26 02 2015 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. 11
Resulting AsterLens Visualizations

12. 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. 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. 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. 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. 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