Jeff Morris, VP of Product & Solutions Marketing at Couchbase, joins Corey on Screaming in the Cloud to discuss Couchbase’s new columnar data store functionality, specific use cases for columnar data stores, and why AI gets better when it communicates with a cleaner pool of data. Jeff shares how more responsive databases could allow businesses like Dominos and United Airlines to create hyper-personalized experiences for their customers by utilizing more responsive databases. Jeff dives into the linked future of AI and data, and Corey learns about Couchbase’s plans for the re:Invent conference. If you’re attending re:Invent, you can visit Couchbase at booth 1095.About JeffJeff Morris is VP Product & Solutions Marketing at Couchbase (NASDAQ: BASE), a cloud database platform company that 30% of the Fortune 100 depend on.Links Referenced:Couchbase: https://www.couchbase.com/TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: Welcome to Screaming in the Cloud. I’m Corey Quinn. This promoted guest episode of Screaming in the Cloud is brought to us by our friends at Couchbase. Also brought to us by Couchbase is today’s victim, for lack of a better term. Jeff Morris is their VP of Product and Solutions Marketing. Jeff, thank you for joining me.Jeff: Thanks for having me, Corey, even though I guess I paid for it.Corey: Exactly. It’s always great to say thank you when people give you things. I learned this from a very early age, and the only people who didn’t were rude children and turned into worse adults.Jeff: Exactly.Corey: So, you are effectively announcing something new today, and I always get worried when a database company says that because sometimes it’s a license that is going to upset people, sometimes it’s dyed so deep in the wool of generative AI that, “Oh, we’re now supporting vectors or whatnot.” Well, most of us don’t know what that means.Jeff: Right.Corey: Fortunately, I don’t believe that’s what you’re doing today. What have you got for us?Jeff: So, you’re right. It’s—well, what I’m doing is, we’re announcing new stuff inside of Couchbase and helping Couchbase expand its market footprint, but we’re not really moving away from our sweet spot, either, right? We like building—or being the database platform underneath applications. So, push us on the operational side of the operational versus analytic, kind of, database divide. But we are announcing a columnar data store inside of the Couchbase platform so that we can build bigger, better, stronger analytic functionality to feed the applications that we’re supporting with our customers.Corey: Now, I feel like I should ask a question around what a columnar data store is because my first encounter with the term was when I had a very early client for AWS bill optimization when I was doing this independently, and I was asking them the… polite question of, “Why do you have 283 billion objects in a single S3 bucket? That is atypical and kind of terrifying.” And their answer was, “Oh, we built our own columnar data store on top of S3. This might not have been the best approach.” It’s like, “I’m going to stop you there. With no further information, I can almost guarantee you that it was not.” But what is a columnar data store?Jeff: Well, let’s start with the, everybody loves more data and everybody loves to count more things, right, but a columnar data store allows you to expedite the kind of question that you ask of the data itself by not having to look at every single row of the data while you go through it. You can say, if you know you’re only looking for data that’s inside of California, you just look at the column value of find me everythin