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Your Path to Big Data Sucess
Your Path to Big Data Sucess
Your Path to Big Data Sucess
Your Path to Big Data Sucess
Your Path to Big Data Sucess
Your Path to Big Data Sucess
Your Path to Big Data Sucess
Your Path to Big Data Sucess
Your Path to Big Data Sucess
Your Path to Big Data Sucess
Your Path to Big Data Sucess
Your Path to Big Data Sucess
Your Path to Big Data Sucess
Your Path to Big Data Sucess
Your Path to Big Data Sucess
Your Path to Big Data Sucess
Your Path to Big Data Sucess
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Your Path to Big Data Sucess

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  • IN THIS SESSION, WE WILL EXPLORE USING HADOOP TO ADDRESS QUESTIONS AND ISSUES SURROUNDING * Cost of storage * Value of accessibility * Getting maximum return on your IT investments and all of your data
  • Transcript

    • 1. Your Path to Success with Big Data
    • 2. 3 The Typical Business Intelligence Data Stack 3 BI / Reporting EDW Transformation (ETL) Staging / Storage Collection
    • 3. 4 Step 1: EDH for Storage/Staging/Active Archive 4 BI / Reporting EDW Transformation (ETL) EDH for Storage Active Archive Collection
    • 4. 5 EDH for Collection & Storage. Step 1: EDH for Storage/Staging/Active Archive 5 BI / Reporting EDW Transformation (ETL)
    • 5. 6 Step 3: EDH for Transformation Acceleration 6 EDW EDH for Collection, Storage & Transformation Acceleration. ETL / Data Integration Tools BI / Reporting
    • 6. 7 EDH for Collection, Storage, Transformation Acceleration & historical EDW data/queries. Step 4: EDH for EDW Optimization (Impala) 7 BI / Reporting EDW Rarely Used Data
    • 7. 8 Step 4: EDH for EDW Optimization (Impala) 8 EDW BI / Reporting Agile Exploration EDH for Collection, Storage, Transformation Acceleration & historical EDW data/queries.
    • 8. 9 Step 6: EDH for Data Science (Oryx/Spark) 9 EDH for Collection, Storage, Transformation Acceleration & historical EDW data/queries. EDW BI / Reporting Agile Exploration Data Science
    • 9. 10 Step 7: Full Consolidation - Apps Come to Data 10 EDW BI Explore Data Science SAS, R, Spark Informatica SyncSort, Pentaho Hunk ... EDH for Collection, Storage, Transformation Acceleration & historical EDW data/queries.
    • 10. 11 Data ScienceExploration ETL Acceleration Operational Efficiency Information Advantage Cheap Storage BusinessIT Journey to Achieve Full Potential ©2014 Cloudera, Inc. All Rights Reserved. EDW Optimization Consolidation 360° View
    • 11. 12 WEB/MOBILE APPLICATION ONLINE SERVING SYSTEM ENTERPRISE DATA WAREHOUSE ENTERPRISE REPORTINGBI / ANALYTICSDATA MODELINGDEVELOPER TOOLS CLOUDERA MANAGER META DATA / ETL TOOLS ENTERPRISE DATA HUB The Modern Information Architecture ©2014 Cloudera, Inc. All Rights Reserved.12 Data Architects System Operators Engineers Data Scientists Analysts Business Users Customers & End Users SYS LOGS WEB LOGS FILES RDBMS
    • 12. 13 Data Warehouse vs. Data Hub ©2014 Cloudera, Inc. All Rights Reserved. Enterprise Data Warehouse Enterprise Data Hub
    • 13. 14 BI and Analytics Partners Enabling The App Store of Big Data SI, Cloud, MSP Partners Database Partners Resellers Data Integration Partners Hardware Partners
    • 14. 15 Customer Success Across Industries Financial & Business Services Telecom Technology Healthcare Life Sciences Media Retail Consumer Energy Public Sector
    • 15. 16 Conclusion: An Enterprise Data Hub Allows You To • Active Archive • Retain “Option Value” of Data • Accelerate ETL Transformations • Enable Exploration/Agility • Consolidate Silos • Achieve True 360 View of Customers and Products. ©2014 Cloudera, Inc. All Rights Reserved.
    • 16. Thank You! 17

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