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

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

    • 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
    • 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 Fastest MPP Database Huge library of Analytical Functions 3
    • 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
    • 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
    • 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 – Q2 2013 8
    • 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
    • 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 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • Today’s demonstration Connect Hadoop Transform Data Actian Dataflow Parallel Load Fast Database Queries Actian Vector Fast Analysis BI Visualization Layer Yellowfin BI 17
    • 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 demands deeper insights 19
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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 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
    • 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
    • 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