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PANEL SENIOR BIG DATA ARCHITECT BD-COE

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Presentation on theme: "PANEL SENIOR BIG DATA ARCHITECT BD-COE"— Presentation transcript:

1 PANEL DAVID.WINTERS@TERADATA.COM SENIOR BIG DATA ARCHITECT BD-COE DAVID.WINTERS@TERADATA.COM

2 Confidential and proprietary. Copyright © 2012 Teradata Corporation. 2 When to Use Which? The best approach by workload and data type Processing as a Function of Schema Requirements and Stage of Data Pipeline Low Cost Storage and Fast Loading Data Pre- Processing, Refining, Cleansing “Simple math at scale” (Score, filter, sort, avg., count...) Joins, Unions, Aggregates Analytics (Iterative and data mining) Reporting Stable Schema Evolving Schema Aster (SQL + MapReduce Analytics) Format, No Schema Hadoop Aster (MapReduce Analytics) Teradata/ Hadoop Teradata Hadoop Aster / Hadoop Aster Hadoop Aster Financial Analysis, Ad-Hoc/OLAP Enterprise-Wide BI and Reporting Spatial/Temporal Active Execution Interactive Data Discovery Web Clickstream, Set-Top Box Analysis CDRs, Sensor Logs, JSON Social Feeds, Text, Image Processing Audio/Video Storage and Refining Storage and Batch Transformation s

3 Confidential and proprietary. Copyright © 2012 Teradata Corporation. 3 When to Use which data engine? The best approach by workload and data type Processing as a Function of Schema Requirements by Data Low Cost Storage and Fast Loading Data Pre- Processing, Refining, Cleansing “Simple math at scale” (Score, filter, sort, avg., count...) Joins, Unions, Aggregates Reporting Analytics (Iterative and data mining) Stable Schema Evolving Schema A-DBMS (SQL + MapReduce Analytics) Format, No Schema Hadoop A-DBMS (MapReduce Analytics) EDW/ Hadoop EDW (SQL analytics) Hadoop A-DBMS / Hadoop A-DBMS (SQL + MapReduce Analytics) Hadoop A-DBMS (MapReduce Analytics) Need Schema

4 Confidential and proprietary. Copyright © 2012 Teradata Corporation. 4 Analytic_DBMS – Hadoop - EDW RequirementsA-DBMSHadoopEDW MapReduce integration Interactive user tools Complex analytics (e.g. time-series, graph, social network) UDF Multi-language support (Java, R, Python, Perl, SAS, scripts, Bash, C+) UDF Programming flexibility and ease UDF Performance Integrated data System management, WLM Labor costs Concurrent users 10-1001-10200-1000 ExcellentPoor Good Very GoodFair Note: +¼ moon can mean years of investment

5 Confidential and proprietary. Copyright © 2012 Teradata Corporation. 5 END


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