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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