The document outlines the use of Actian and Yellowfin technologies to simplify and accelerate big data analytics using Hadoop, addressing common challenges associated with traditional analytics. It highlights customer use cases, particularly within the telecom industry, showcasing the platform's capabilities for data processing, real-time insights, and scalability. The presentation emphasizes the need for efficient data preparation and analysis to enable actionable business insights while minimizing operational complexities.

1. December 16, 2013
Making Big Data Analytics
Fast and Easy
Using Actian, Yellowfin and Hadoop
John Ryan
Ryan Templeton
Ivan Seow
Marketing Manager APAC
Actian Corporation
Snr Solutions Architect
Actian Corporation
Snr Technical Consultant
Yellowfin

2. Take Action on Big Data
Making BI Easy
2

3. Take Action on Big Data
Making BI Easy
Fastest Data Prep Engine
Fastest Hadoop Loader
Fastest Single Node Database
Fastest MPP Database
Huge library of Analytical Functions
3

4. Take Action on Big Data
Making BI Easy
Fastest Data Prep Engine
Ranked #1 BI Vendor
Dresner Global BI Study 2012 & 13
Fastest Hadoop Loader
#1 Dashboard Vendor:
BARC BI Survey 12
Fastest Single Node Database
Fastest MPP Database
#1 Enterprise Reporting Vendor:
BARC BI Survey 13
Huge library of Analytical Functions
Gartner: ‘Vendor to Consider’
4

5. Today’s Agenda
1. Big Data Analytics with Hadoop
2. Making Analytics in Hadoop Fast & Easy
3. Customer Example (Telecom)
4. Demo: From Data to Dashboard
•
•
Making Hadoop Fast and Easy
Making BI Fast and Easy
5. Summary
5

6. Big Data Analytics
With Hadoop
Confidential © 2012 Actian Corporation
6

7. 73%
Expect to have HDFS
in production
Based on 263 respondents
TDWI Best Practices Report – Q2 2013
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8. 71%
Big Data Source for Analytics
Most Likely to Benefit from Hadoop
Based on 263 respondents
TDWI Best Practices Report – Q2 2013
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9. Why is analytics inside Hadoop
so hard and slow?
HDFS is a file system,
not a database
Need a Data Scientist
Queries not standard SQL,
only resemble SQL
MapReduce inefficient
for analytic queries
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10. Making Big Data with
Hadoop Fast and Easy
With Actian and Yellowfin
Confidential © 2012 Actian Corporation
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11. Actian Big Data Analytic Platform
Big Data Storage
Business
Intelligence
Accelerating Big Data 2.0
Connect
Prepare
Optimize
Analyze
Enterprise
VALUE
DATA
Applications
DW
Advanced technology platform:
Multiple deployment options:
Industry leading:
 On-premise
 Scale
 Cloud
 Performance
 Hybrid
 Complexity
 Embedded
 Cost (price/performance)
 Time to Value
11

12. Actian Big Data Analytic Platform
Big Data Storage
Business
Intelligence
Accelerating Big Data 2.0
Connect
Prepare
Optimize
Analyze
Enterprise
VALUE
DATA
Applications
DW
Advanced technology platform:
Multiple deployment options:
Industry leading:
 On-premise
 Scale
 Cloud
 Performance
 Hybrid
 Complexity
 Embedded
 Cost (price/performance)
 Time to Value
12

13. Actian Big Data Analytic Platform
Big Data Storage
Business
Intelligence
Accelerating Big Data 2.0
Connect
Prepare
Optimize
Analyze
Enterprise
VALUE
DATA
Applications
DW
Advanced technology platform:
Multiple deployment options:
Industry leading:
 On-premise
 Scale
 Cloud
 Performance
 Hybrid
 Complexity
 Embedded
 Cost (price/performance)
 Time to Value
13

14. Actian Big Data Analytic Platform
Big Data Storage
Business
Intelligence
Accelerating Big Data 2.0
Connect
Prepare
Optimize
Analyze
Enterprise
VALUE
DATA
Applications
DW
Advanced technology platform:
Multiple deployment options:
Industry leading:
 On-premise
 Scale
 Cloud
 Performance
 Hybrid
 Complexity
 Embedded
 Cost (price/performance)
 Time to Value
14

15. Actian Big Data Analytic Platform
Big Data Storage
Business
Intelligence
Accelerating Big Data 2.0
Connect
Prepare
Optimize
Analyze
Enterprise
VALUE
DATA
Applications
DW
Advanced technology platform:
Multiple deployment options:
Industry leading:
 On-premise
 Scale
 Cloud
 Performance
 Hybrid
 Complexity
 Embedded
 Cost (price/performance)
 Time to Value
15

16. Industry Leading Performance
Process Hadoop Data Faster
Analyze Data Faster
Dataflow vs PIG (MapReduce)
Database Benchmarks
DBT-3@1TB : Run times
TPC-H QphH@1TB Benchmarks (non-clustered)
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17. Today’s demonstration
Connect
Hadoop
Transform
Data
Actian Dataflow
Parallel
Load
Fast
Database
Queries
Actian Vector
Fast
Analysis
BI Visualization Layer
Yellowfin BI
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18. Telecom Example
Storing CDR Log Files inside Hadoop
Confidential © 2012 Actian Corporation
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19. Customer Use Case
 Tier two telecom provider
 Planning for large growth
with minimal staff impact
 Business demands deeper insights
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20. IT Challenges
Collect, manage, process
CDR data in Hadoop
Swamped with data.
Network switch dumps 200MB /min
during peak times.
Hundreds of thousands of records per drop.
170 columns.
Users are domain experts,
not data scientists
Too hard to analyze
Raw data must first be distilled
and enriched to gain insight
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21. What the business was asking for
Fastest time to
decision
Speed up processing by an order of magnitude
Increased granularity
of analysis
Without increasing processing times or bogging down
backend
Proactive analysis,
not reactive
Enable trend analysis and predictive capabilities
Answer real
business questions
e.g. visual insight for near real-time customer and vendor
performance, determine routing performance
optimization, etc
Scale for future
growth
Extensible for future capabilities and scalable growth
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22. Specific Business Questions - CDR Analysis
 Answer Service Rate (ASR & Adjusted ASR)
• Calls completed vs. route attempts (vendor performance)
• Calls completed vs. call attempts (customer satisfaction)
 Opportunity Monitor
• Calculate profit/loss per call due to routing path chosen
 Post Dial Delay (PDD)
• Annoying delay until path through network selected
 Analysis of near real time quality measures
• Call duration, jitter and packet loss
 Trends and correlations of above metrics
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23. CDR Workflow Overview
CONNECT
TRANSFORM
Filter data Logical functions
Extract failed
routing attempts
Split flow for separate
processing rules
Meta-node
encapsulates
processing
PARALLEL
DATA
LOAD
23

24. Data processing – Execution Plan
Compiled to a set
of physical graphs
Phase 1
Phase 2
Reader
FilterRows
DeriveFields
Group(partial)
Repartition
Group(final)
Writer
Reader
FilterRows
DeriveFields
Group(partial)
Repartition
Group(final)
Writer
Reader
FilterRows
DeriveFields
Group(partial)
Repartition
Group(final)
Writer
Reader
FilterRows
DeriveFields
Group(partial)
Repartition
Group(final)
Writer
24

25. Demo
Making Big Data Analytics Fast and Easy
Confidential © 2012 Actian Corporation
25

26. Customer Take Aways – Actionable Insights
FAST
Processing streaming
CDR data in seconds
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27. Customer Take Aways - Analysis
Deeper
Analysis
visibility at the Area Code
and Exchange level
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28. Customer Take Aways – Cost Savings
20,000
updates made to routing
tables during first week
of collecting data
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29. Customer Take Aways - Scalability
8.9 Billion
rows of data collected
during first 6 months
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30. Solution Architecture
Clustered Execution
Hadoop
Collection
Parallel Loading
Paraccel
Dataflow
Vectorwise
Very fast reporting
database
Extraction
Cleansing
Yellowfin BI
End Users
• Dashboard
• Ad Hoc
• Statistics
• Data Mining
• Analytics
Desktop &
Mobile Devices
Enrichment
Aggregation
Data
Retention
Analysis
Mining
OSS/BSS
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30

31. Summary – Take Action on Big Data
Big Data Storage
Business
Intelligence
Accelerating Big Data 2.0
Connect
Prepare
Optimize
Analyze
Enterprise
VALUE
DATA
Applications
DW
Advanced technology platform:
Multiple deployment options:
Industry leading:
 On-premise
 Scale
 Cloud
 Performance
 Hybrid
 Complexity
 Embedded
 Cost (price/performance)
 Time to Value
31

32. Actian
Ivan Seow
www.actian.com
Ivan.Seow@Yellowfin.BI
Yellowfin
John Ryan
www.Yellowfin.bi
John.Ryan@actian.com
Ryan Templeton
Ryan.Templeton@actian.com
Questions
Confidential © 2012 Actian Corporation
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