Archive for Big Data category

COVID19 and New Life

I’ve been doing a ton of writing and speaking, just not here. My first post on Medium is on COVID-19 myths and has a ton of links to reliable data sources to help dispel them. I’ve been writing some on the Vertica blog, doing a few projects for O’Reilly, and I’ve been writing my usual web content and technical architecture papers. But the main thing I’ve spent my time on in the last couple of years is public speaking. I was set to travel 5 weeks out of the last six to speak at conferences. That didn’t exactly happen.

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Paige Roberts presenting to full room with Zynga case study slide showing

DBTA Data Summit – The Rise of DataOps

The recent DBTA Data Summit provided a lot to think about. I did a short talk in the “Analytics in Action” track about how data analysts, architects and engineers can turn the endless waves of disruption we keep getting hit with into opportunities to boost bottom line. There were some very cool talks by other folks as well. For me, the highlights of the conference were Michael Stonebraker’s keynote, and the Data Kitchen folks diving into the principles of DataOps.

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Davin Potts, CEO Appliomics, Founder KNIME, Core Python Commiter

One on One with Davin Potts

At the recent Data Day Texas event, I sat down with Davin Potts, who I have known for many years, and had a long conversation about a wide variety of subjects. Over on the Vertica blog, I broke the conversation into chunks, but I wanted to put it all together in one place so you can see what we chatted about end to end. So, here’s all of it, from machine learning to open source, from Python to Knime, and why the heck DO we move data out of a database to analyze it?

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Orc O'Malley of the Yellow Elephant clan says LLAP

Owen O’Malley on the Origins of Hadoop, Spark and a Vulcan ORC

Owen O’Malley is one of the folks I chatted with at the last Hadoop Summit in San Jose. I already discovered the first time I met him that he was the big Tolkien geek behind the naming of ORC files, as well as making sure that Not All Hadoop Users Drop ACID. In this conversation, I learned that Hadoop and Spark are both partially his fault, about the amazing performance strides Hive with ORC, Tez and LLAP have made, and that he’s a Trek geek, too.

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