Escaping the tyranny of “OR” in Analytics

Running any large-scale organization is about data, specifically, big data. While it has never been easier to collect and store data, when it comes to analyzing data, users face limited options. They need to decide between using a parallel data warehouse and a big data framework. In this talk we will present how Amazon Redshift combines the best of both worlds with Spectrum, a feature of Redshift. We will present the architecture of Redshift and Spectrum, how Redshift processes fast queries from both local disks and Amazon S3, and discuss challenges around and opportunities enabled with this architecture.

Ippokratis Pandis  is a principal engineer at Amazon Web Services, currently working on Amazon Redshift. Redshift is Amazon's fully managed, petabyte-scale data warehouse service. Redshift Spectrum enables running Redshift queries against even larger volumes of data in Amazon S3. Previously, Ippokratis has held positions as software engineer at Cloudera and member of the research staff at IBM Almaden Research Center. Ippokratis received his PhD from the Electrical and Computer Engineering department at Carnegie Mellon University. He is the recipient of Best Demonstration awards at ICDE 2006 and SIGMOD 2011, Best Paper award at CIDR 2013 and Test-of-Time award at EDBT 2019. He has served as PC chair of DaMoN 2014, DaMoN 2015, CloudDM 2016, NorCal DB Day 2017, and HPTS 2019.