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Getting Data Mastering at Scale Right

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Manage episode 375846758 series 3482550
Content provided by Nandi Ngubentombi. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Nandi Ngubentombi or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

What’s required to master large numbers of data sources? First, avoid approaches that require writing rules. Then use machine learning and cloud computing to efficiently handle the workload. That advice comes from Mike Stonebraker, a database pioneer who helped create the INGRES relational database system, won the 2014 A.M. Turing Award, and has co-founded several data management startups, including Tamr. Mike talks about common data mastering mistakes, why traditional tools aren’t right for the task, and shares examples of companies that have successful mastered data at scale.

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32 episodes

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iconShare
 
Manage episode 375846758 series 3482550
Content provided by Nandi Ngubentombi. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Nandi Ngubentombi or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

What’s required to master large numbers of data sources? First, avoid approaches that require writing rules. Then use machine learning and cloud computing to efficiently handle the workload. That advice comes from Mike Stonebraker, a database pioneer who helped create the INGRES relational database system, won the 2014 A.M. Turing Award, and has co-founded several data management startups, including Tamr. Mike talks about common data mastering mistakes, why traditional tools aren’t right for the task, and shares examples of companies that have successful mastered data at scale.

  continue reading

32 episodes

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