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December 16, 2013

Making Big Data Analytics
Fast and Easy
Using Actian, Yellowfin and Hadoop

John Ryan

Ryan Templeton

...
Take Action on Big Data

Making BI Easy

2
Take Action on Big Data

Making BI Easy

Fastest Data Prep Engine
Fastest Hadoop Loader
Fastest Single Node Database
Faste...
Take Action on Big Data

Making BI Easy

Fastest Data Prep Engine

Ranked #1 BI Vendor
Dresner Global BI Study 2012 & 13

...
Today’s Agenda
1.  Big Data Analytics with Hadoop
2.  Making Analytics in Hadoop Fast & Easy
3.  Customer Example (Telecom...
Big Data Analytics
With Hadoop
Confidential © 2012 Actian Corporation

6
73%

Expect to have HDFS
in production

Based on 263 respondents
TDWI Best Practices Report – Q2 2013
7
71%

Big Data Source for Analytics
Most Likely to Benefit from Hadoop

Based on 263 respondents
TDWI Best Practices Report...
Why is analytics inside Hadoop
so hard and slow?

HDFS is a file system,
not a database

Need a Data Scientist

Queries no...
Making Big Data with
Hadoop Fast and Easy
With Actian and Yellowfin
Confidential © 2012 Actian Corporation

10
Actian Big Data Analytic Platform
Big Data Storage

Business
Intelligence

Accelerating Big Data 2.0

Connect

Prepare

Op...
Actian Big Data Analytic Platform
Big Data Storage

Business
Intelligence

Accelerating Big Data 2.0

Connect

Prepare

Op...
Actian Big Data Analytic Platform
Big Data Storage

Business
Intelligence

Accelerating Big Data 2.0

Connect

Prepare

Op...
Actian Big Data Analytic Platform
Big Data Storage

Business
Intelligence

Accelerating Big Data 2.0

Connect

Prepare

Op...
Actian Big Data Analytic Platform
Big Data Storage

Business
Intelligence

Accelerating Big Data 2.0

Connect

Prepare

Op...
Industry Leading Performance
Process Hadoop Data Faster

Analyze Data Faster

Dataflow vs PIG (MapReduce)

Database Benchm...
Today’s demonstration

Connect
Hadoop

Transform
Data

Actian Dataflow

Parallel
Load

Fast
Database
Queries

Actian Vecto...
Telecom Example
Storing CDR Log Files inside Hadoop
Confidential © 2012 Actian Corporation

18
Customer Use Case
  Tier two telecom provider

  Planning for large growth
with minimal staff impact

  Business demand...
IT Challenges

Collect, manage, process
CDR data in Hadoop

Swamped with data.
Network switch dumps 200MB /min
during peak...
What the business was asking for
Fastest time to
decision

Speed up processing by an order of magnitude

Increased granula...
Specific Business Questions - CDR Analysis
  Answer Service Rate (ASR & Adjusted ASR)
•  Calls completed vs. route attemp...
CDR Workflow Overview
CONNECT

TRANSFORM
Filter data Logical functions

Extract failed
routing attempts

Split flow for se...
Data processing – Execution Plan
Compiled to a set
of physical graphs

Phase 1

Phase 2

Reader

FilterRows

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

25
Customer Take Aways – Actionable Insights

FAST
Processing streaming
CDR data in seconds

26
Customer Take Aways - Analysis

Deeper
Analysis
visibility at the Area Code
and Exchange level

27
Customer Take Aways – Cost Savings

20,000

updates made to routing
tables during first week
of collecting data

28
Customer Take Aways - Scalability

8.9 Billion
rows of data collected
during first 6 months

29
Solution Architecture
Clustered Execution

Hadoop
Collection

Parallel Loading

Paraccel
Dataflow

Vectorwise
Very fast re...
Summary – Take Action on Big Data
Big Data Storage

Business
Intelligence

Accelerating Big Data 2.0

Connect

Prepare

Op...
Actian

Ivan Seow

www.actian.com

Ivan.Seow@Yellowfin.BI

Yellowfin

John Ryan

www.Yellowfin.bi

John.Ryan@actian.com

R...
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Making Big Data Analytics with Hadoop fast & easy (webinar slides)

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Looking to analyze your Big Data assets to unlock real business benefits today? But, are you sick of all the theories, hype and whoopla?

View these slides from Actian and Yellowfin’s "Big Data Analytics with Hadoop" Webinar to discover how we’re making Big Data Analytics fast and easy.

Hold on as we go from data in Hadoop to dashboard in just 40-minutes.

Learn how to combine Hadoop with the most advanced Big Data technologies, and world’s easiest BI solution, to quickly generate real business value from Big Data Analytics.

Watch as we use live CDR data stored in Hadoop – quickly connecting, preparing, optimizing and analyzing this data in a tangible real-world use case from the telecommunications industry – to easily deliver actionable insights to anyone, anywhere, anytime.

To learn more about Yellowfin, and to try its intuitive Business Intelligence platform today, go here: http://www.yellowfinbi.com

To learn more about Actian, and its next generation suite of Big Data technologies, go here: http://www.actian.com/

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Transcript of "Making Big Data Analytics with Hadoop fast & easy (webinar slides)"

  1. 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. 2. Take Action on Big Data Making BI Easy 2
  3. 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. 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. 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. 6. Big Data Analytics With Hadoop Confidential © 2012 Actian Corporation 6
  7. 7. 73% Expect to have HDFS in production Based on 263 respondents TDWI Best Practices Report – Q2 2013 7
  8. 8. 71% Big Data Source for Analytics Most Likely to Benefit from Hadoop Based on 263 respondents TDWI Best Practices Report – Q2 2013 8
  9. 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 9
  10. 10. Making Big Data with Hadoop Fast and Easy With Actian and Yellowfin Confidential © 2012 Actian Corporation 10
  11. 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. 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. 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. 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. 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. 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) 16
  17. 17. Today’s demonstration Connect Hadoop Transform Data Actian Dataflow Parallel Load Fast Database Queries Actian Vector Fast Analysis BI Visualization Layer Yellowfin BI 17
  18. 18. Telecom Example Storing CDR Log Files inside Hadoop Confidential © 2012 Actian Corporation 18
  19. 19. Customer Use Case   Tier two telecom provider   Planning for large growth with minimal staff impact   Business demands deeper insights 19
  20. 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 20
  21. 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 21
  22. 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 22
  23. 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. 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. 25. Demo Making Big Data Analytics Fast and Easy Confidential © 2012 Actian Corporation 25
  26. 26. Customer Take Aways – Actionable Insights FAST Processing streaming CDR data in seconds 26
  27. 27. Customer Take Aways - Analysis Deeper Analysis visibility at the Area Code and Exchange level 27
  28. 28. Customer Take Aways – Cost Savings 20,000 updates made to routing tables during first week of collecting data 28
  29. 29. Customer Take Aways - Scalability 8.9 Billion rows of data collected during first 6 months 29
  30. 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 30 30
  31. 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. 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 32
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