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P.QL: Guaranteed Performance Query Language for Ever-Growing Data

Learn how P.QL ensures performance in web applications and tackles complexity in querying ever-growing data sets, presenting a solution independent of scale with SQL-like syntax. Explore the state of big data analytics from Watson/IBM to Berkeley Data Analysis System (BDAS).

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P.QL: Guaranteed Performance Query Language for Ever-Growing Data

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  1. Guaranteed Performance While Querying Ever-Growing Data P QL Michael Armbrust BEARS Conference – February 2012

  2. Web Applications Grow Rapidly

  3. Success Disaster?

  4. Force developers to use simple operations (get/put) • Makes complexity obvious

  5. Force developers to use simple operations (get/put) • No optimization or data independence

  6. PIQLSolution • Performance Insightful Query Language • SQL-like • Builds on existing scalable storage • Guaranteed performance independent of scale P QL

  7. Problem with Cost Based Optimization Plan Choices Sequential Scan Random Lookups

  8. BIG DATA: The State of the Art Watson/IBM Algorithms search Machines People

  9. AMP: A Holistic Approach search Algorithms Watson/IBM Machines People

  10. BDAS: Berkeley Data Analysis System A Top-to-Bottom Rethinking of the big data analytics stack integrating Algorithms, Machines, and People Data Collector Algo/Tools Data Analyst Infra. Builder Data Source Selector Result Control Center Visualization Analytics Libraries, Data Integration Poster Session @amplab 465 SODA Hall Higher Query Languages / Processing Frameworks Monitoring/Debugging Quality Control Resource Management Crowd Interface Storage Data Collector

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