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Panel David.winters@teradata Senior Big Data Architect BD-COE

Panel David.winters@teradata.com Senior Big Data Architect BD-COE. When to Use Which? The best approach by workload and data type. Processing as a Function of Schema Requirements and Stage of Data Pipeline. Financial Analysis, Ad-Hoc/ OLAP Enterprise-Wide BI and Reporting Spatial/Temporal

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Panel David.winters@teradata Senior Big Data Architect BD-COE

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  1. PanelDavid.winters@teradata.comSenior Big Data ArchitectBD-COE

  2. When to Use Which?The bestapproach by workload and data type Processing as a Function of Schema Requirements and Stage of Data Pipeline Financial Analysis, Ad-Hoc/OLAP Enterprise-Wide BI and Reporting Spatial/Temporal Active Execution Teradata/Hadoop Teradata Teradata Teradata Teradata Teradata Interactive Data Discovery Web Clickstream, Set-Top Box Analysis CDRs, Sensor Logs, JSON Hadoop Aster / Hadoop Aster /Hadoop Aster Aster Aster Social Feeds, Text, Image Processing Audio/Video Storage and Refining Storage and Batch Transformations Hadoop Hadoop Hadoop Aster Aster Aster

  3. When to Use which data engine? The best approach by workload and data type • Processing as a Function of Schema Requirements by Data • Need • Schema EDW/Hadoop EDW EDW EDW EDW EDW (SQL analytics) Hadoop A-DBMS / Hadoop A-DBMS /Hadoop A-DBMS A-DBMS A-DBMS (SQL + MapReduce Analytics) Hadoop Hadoop Hadoop A-DBMS A-DBMS A-DBMS (MapReduce Analytics)

  4. Analytic_DBMS – Hadoop - EDW Excellent Very Good Fair Poor Good Note: +¼ moon can mean years of investment

  5. END

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