Special Episode - Decoding the Snowflake Data Cloud Summit with Mike Cochrane

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Hi, Sandy here. Welcome to another episode of How I Met Your Data.

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This week, Anjali and I have a special edition for you. We're going to debrief

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on the Snowflake Data Cloud Summit that took place just a couple of weeks ago.

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And in order to do that effectively, we actually asked one of my colleagues,

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Michael Cochran, to join us.

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Mike is currently Cervelo's Global Data and Analytics Consulting Practice Leader.

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He is someone I have worked on and off with for over 22 years of my career,

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and someone I deeply admire.

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So I met Mike in 2002 at a company called Painted Word, where we both started

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off there as front-end developers.

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Our roads absolutely diverged. Mike went from front-end development to wanting

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to learn a little bit about the back end.

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So he dove into data warehousing and data warehousing concepts.

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I remember just seeing his nose deep deep into books at his cubicle for days

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on end, if not months on end, as he got all his certifications back in the day.

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He also became a software developer.

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He used to build most of our custom solutions, our web custom solutions as well

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during the later part of our career there.

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He moved on and became the CIO for the Palladium Group, where he learned all

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the challenges CIOs his face by living in that role day to day.

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He did that for a number of years.

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He then joined Cervelo as the first employee and non-founding member of the

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firm, where he was quickly asked to think through the cloud strategy and look

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at all the technologies that were coming to fruition about 14 years ago when we were founded.

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So he did that and quickly culminated into him becoming the Global Data and

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Analytics Consulting Practice Lead,

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which he co-leads today with another colleague of ours, Glenn Heatley.

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So now that you have that background on Mike, we can go ahead and dive into the conversation.

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So with that said, let's dive in.

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Music.

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Hey, Mike. Hey, Mike. Hi. Welcome. How are you? Wait, this is recording already? already.

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Yes, we're recording already. You got to give me a heads up.

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There's no video. Just walking right into the show.

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We like to keep it spontaneous.

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Welcome, Mike. Thanks for joining us. I know you and Sandy just recently got

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back and have recovered from your time at the Snowflake Summit.

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Cervelo has been a Snowflake partner since the very beginning, I think.

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2016, I think. They went GA in 2014.

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We were about a year into that. So it was probably late sometime in 2015.

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And then really pushed it in 2016. Great. Cervelo has been a partner for a number of years.

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And I think there's been a lot of changes that we've experienced in the platform

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in the way that our clients are really deriving value.

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So I'd love to hear from you. What have you seen as that snowflake evolution

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that's occurred and how has it been impacting our clients?

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Yeah, it's a great question. I think it's been a pretty interesting journey to date.

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When we were first introduced to Snowflake, I think my impression was that here

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was a company that finally addressed how to build a data and analytics solution

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in the cloud that is optimized for the cloud.

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I think up until that point, which Cervelo was founded in 2009,

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we really dove into our cloud strategy in 2010.

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And what you saw was a number of different companies that were taking legacy

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technology, and then just kind of putting it in the cloud and then calling it

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cloud software or cloud platform.

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And it really wasn't optimized for that. You ran into all kinds of performance

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issues, you ran into scalability, you ran into portability challenges,

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it's just the list sort of went on, right?

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So you always kind of ran into a brick wall with a lot of these different companies.

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And when we first saw the demo of Snowflake, we were blown away.

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It was all right, this is going to address this problem, this is going to help

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this client, the number of things that we saw value in that could really contribute

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to our client success was pretty amazing.

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And, you know, I remember I was being in a QBR in New York with them early on in our relationship.

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And somebody asked me the question, hey, what are your thoughts?

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What should we be thinking about?

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I said, listen, you know, your platform and your technology is great,

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but you've got a limited window where people are going to start to catch up.

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And therefore, it's like, what's the next thing? And what's the next thing?

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So as I think about where they've really come over over the last however many

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years, think they've sort of gone down that strategy path, right?

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They started with, all right, here's a technology that's built and born in the

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cloud, and then really realized that, okay, our clients have other needs and

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use cases that we need to close the gap on.

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And so, you know, seeing them add things like multi-cloud support.

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So getting beyond just AWS, opening up for Azure and Google, And now more recently,

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being able to combine all of those capabilities in more of a hybrid structure

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is pretty impactful for many enterprise clients that have a multi-cloud or hybrid strategy.

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And so it's been great to see that evolve over time.

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The idea of clients having the demands of we need data in real time, right?

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You always had to dig into that need or that requirement because it wasn't always

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real time. It was, all right, I need it just in time or at the right time.

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But now the way that technology is advanced, real time is a reality and it's

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really table stakes now.

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Like if you're not delivering things in real time or near real time,

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you're missing out on many opportunities.

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So their ability to start to close that gap as well has been very interesting.

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And, you know, I think more recently coming out of the conference.

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I always tie it back to what's happening in the market and where people demanding

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probably falls in a few different areas.

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But I'd say the top ones are customers or consumers having more control over their data.

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So privacy is pretty key. And so when you think about what they're doing to

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build in that compliance, to build in that governance, data clean rooms is an example.

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And being able to share data without having to move all kinds of private consumer

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or customer data is pretty important.

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And so seeing them put emphasis behind that when we know it's a big issue for

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our clients and something that they're struggling with and they need help with

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is extremely important.

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Cost management, cost optimization. So FinOps, seeing them make strides there

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as well, been pretty interesting to see.

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And then just the complexities of now there's a big focus in AI and you have

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all these different technologies that you have to make interoperable,

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them going beyond just data and bringing compute in the NVIDIA relationship

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and bringing that to the data versus the data going to the compute and having

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to move all of that around and then building capabilities around apps and.

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So when I talk about complexity, all of these things together add complexity

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to our clients' data architectures.

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And they've really been able to bring all of that together with applications,

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with the governance, with data, with high performing compute that AI applications will need.

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It's just been a pretty remarkable journey seeing all of that come together.

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And then, Sandy, I know you and I were kind of joking about this,

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but the very first conference that we were at, the entire room was probably

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smaller than the partner keynote at the most recent conference.

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So, I mean, that just speaks volumes to how big they've gotten in the ecosystem

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and adoption that's come along with that.

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Yeah, it's funny because when we first, I still remember telling you,

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hey, we need to look at this.

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And if it's as good as what they told me, you need to be all over this.

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Because I had, I remember talking to the rep and they were telling me what it could do.

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And I had a flashback of like the last nine years of my career and then heavy

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nights and weekends talking to architects, DBAs, trying to get them to do something that was basically.

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Impossible and impossible feats of performance that we were asking of them over

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the last nine years prior to Snowflake.

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And then seeing that, I mean, I still remember you and I were like, what?

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Like, what is going on here? But if you fast forward from that point forward,

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I feel that they did have a large

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window of being the only one in the marketplace that really could do.

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And they actually said it in the conference. They said, hey,

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look, we don't just take storage from compute.

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We're separating compute from compute. Nobody has done that.

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I don't think anybody still hasn't done that holistically the way they have.

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So they did have a big time in there in the marketplace where they were the only one.

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But now things are changing with data science and AI and ML and all these other

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capabilities that people are trying to build.

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So they had to accelerate. And I feel that.

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The change we see in them today only happened over the last three years.

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They've been around for eight, nine, right?

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Five years of that, it was just growing a data capability, very traditional

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in the cloud, making it work, making it very different than what people had done in the past.

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But now I think they're really accelerating that and thinking beyond that because that has been solved.

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And I think that that's what's super exciting about this is being able to see

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them take this to a completely different place and truly make it a platform

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where anything you have to do on data, you can do on there.

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Yeah. Yeah. I think it's like a really exciting time to see what is coming out from Snowflake.

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And so that kind of brings me to my next question is you've been back for a

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week now from having been at the summit, you've seen and reflected on some of

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the advancements and releases that were showcased at the summit.

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So what were those things that they were sharing with us? And what were your

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takeaways from those those demos?

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Yeah, the you know, just like any big conference, right? There's there's always the the big reveal.

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So I'd say some of the things I'm most excited about probably falls in a couple camps.

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One is probably the less exciting stuff, you know, to me, protecting our clients

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data to making sure that they're governing it properly.

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If you think about the ability to productionalize AI, which which was a big, big topic.

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Companies really focusing on experimentation and trying to figure out the use cases last year.

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And then this is the year of productionalization.

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I agree and disagree with that. I think many companies are trying to productionalize

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and get the real value out of AI.

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But at the end of the day, AI is only as good as the data that you feed it.

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So it's this whole thing of garbage in, garbage out that we've been talking about for decades.

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It's the same thing with AI. AI doesn't change the fundamentals and the block

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and tackling that needs to happen with your data.

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So I'm happy that they're putting emphasis around the governance and the transparency

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and the compliance aspects of it, because that's going to be very critical as we move forward.

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And as customers and consumers get more control over their data,

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as I had mentioned previously, like that is key.

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And you have to be in a position to be able to react to those realities.

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On the flip side of that, you know, more of the innovation in the AI side,

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the fact that they're pushing on the concept of native apps and containerized services,

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and then bringing a lot of the machine learning and LLM capabilities to be able

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to build these chatbots and other customer or client facing solutions.

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I think it's of high value. I think it's still early days in terms of where

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this will go, but I definitely see them on a great track and a great trajectory.

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Our clients, when we speak to them.

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It's all about speed to market, right? It's speed to market to differentiate.

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It's speed to market to stay relevant. It's speed to market to be able to continue

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to be productive and efficient in the work that you do.

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And so that requires acceleration. That requires applications.

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It doesn't just mean, hey, I need data and then I can do anything with data.

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It's like, well, I need data. I need data for supply chain. I need data for

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finance. I need data for a number of different use cases.

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And so to see that you can now,

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all of this kind of coalescing and the lines being blurred of applications versus

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data and compute versus storage and those things are very important and very

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relevant in the modern world.

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So I'd say those are some of the big takeaways for me.

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One thing I'll be critical of is I was looking for maybe a little bit more of

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a bigger reveal on some of the things. So the things that they've had in private

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preview, I was hoping would become publicly available.

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A couple of them are pretty close, but not quite there yet. And I know those

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have real applicability to some of the things that we're doing with our clients today.

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So I'm really looking forward to when those do become publicly available and

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we can productionalize those for our clients.

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I totally agree with you, Mike. I think one trend, though, I've seen with our

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larger clients is creating kind of a bit of a commodity around data engineering.

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Larger organizations start to look at it as a commodity and less of a thing

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that they have to have really skilled workers in, which is a bit of a challenge.

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So the thing I was excited about with Snowflake was that they're doing things

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like there's a co-pilot, but now that co-pilot is in kind of the SQL window itself.

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So you don't have to go somewhere else to get, is this right?

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You can actually finish your code right in there.

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So the AI is helping you. So a lot of those developer tools,

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they announced a ton of DevOps stuff like monitoring, Git integration.

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Little things like that, that they've decided to shovel into the platform.

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Because the bare bones capability of moving data from one place to another,

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making sure it's transformed effectively, ensuring that it's available to the

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business, that work still needs to be done.

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And the easier Snowflake can make it for your engineer to do the right thing,

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the better everybody's going to be. A lot of their competitors aren't spending

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time there trying to make it easy for the developer.

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So they really are trying to make it easy. And that, to me, left an impression

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because I'm not a very technical person.

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So if you can explain something to me and show it to me in a way that actually

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makes sense and I'm not completely lost in where you are in your windows.

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That's actually pretty enticing because that means you can take a junior developer

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and throw it at them and they'll be able to navigate that. No,

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I tend to agree with you, Sandy.

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And it's interesting because this isn't just isolated to Snowflake.

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I think what you're seeing is that the things that are maybe you would consider

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commodity, right, within that role.

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So maybe connecting to different data publishers, right?

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So like an ERP system or CRM, you know, anywhere where you need to get data,

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essentially, those things have evolved over time with different connectors.

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So you don't have to build those, not that you don't have to build them anymore,

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but there's a whole ecosystem around that.

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The way that you transform and work with data is now evolving.

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Some of the things that you had to build in, in terms of validation controls

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and those types of things, those are becoming a lot more easier.

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So in a lot of ways, the engineering role is becoming more like a data orchestrator

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in a way that just an engineer.

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So it's like somebody who really needs to understand, well, where does my data originate from?

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And then what does it need to look like on the other side?

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And you still need to make sure that all that high quality governance and all

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those things are built in.

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And so I think what we'll see is an evolution of that role.

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But companies like Snowflake are really investing in taking away the inefficient

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work that has to happen, creating the assemblies, creating connections in the databases.

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Repeating the same data quality routines over and over again and reinventing the wheel.

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Things that are kind of table stakes, these companies are taking care of.

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And therefore, data engineers are going to have to think about,

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all right, what does my role start to look like and how can I elevate to that

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role, knowing that some of the commodity things will go away over time.

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And another part of that is the native app framework that they've created,

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because anybody can create an app, if you will, that functions with data.

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And then you can leverage it in flight and snowflake. We have a couple of life

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science clients and everybody's talking about the standard of fire.

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I saw there's a native app that allows you to do that.

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And I was just like, wow, you just put like 20 consulting firms out of business

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with a native app. Well, and that's right.

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I think that and just and then just market awareness. Right.

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So now that they have the marketplace and being able to allow partners,

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allow customers, allow the entire ecosystem to build these applications that

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can address fundamental issues and make those readily available.

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Similar to what we saw with digital natives back in the early 2000s and beyond,

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open source, a lot of the innovation they had, it's sort of akin to that, right?

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Like somebody solves a discrete problem, make it available.

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Some of that's going to be paid. Some of it's going to be free.

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But at the end of the day, it creates this awareness and this transparency to

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allow everyone to evolve and to innovate.

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That's exciting to see because at the end of the day, the more eyeballs you

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can get on a problem and have diversity of thought and diversity of how to solve

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that problem just makes it all the better for all of us. Yeah.

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Yeah. It leaves us room to tackle the harder stuff, right? Not the stuff that

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we've already solved time and time again.

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Exactly. Because at the end of the day, you're going to be solving the specific

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thing for the client, not all the stuff that sits behind it that business people

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don't typically see, right?

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You know, it's that whole iceberg image. I only see the stuff at the top of

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the water, but not all the mess that kind of sits below the surface. That hasn't changed.

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And so it's like, how quickly can I get to that tip of the iceberg and show

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that value to a customer, to a client? That's the important thing.

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And so all this other stuff, automate it as best you can.

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You can't automate everything, but that's really where the world is going, right?

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It's automating a lot of human behaviors that are repeatable.

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And I think what we're seeing is your data engineering point,

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Sandy, doesn't just stop there.

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It goes into analytics and a number of other things.

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There's a lot more automation of that human behavior that we're seeing.

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And so everyone needs to figure out how do I adapt to that? What's my strategy

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to do that? What are the people I'm going to need? How do skill sets evolve?

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How do we train people? How do we deal with the change management aspects of it?

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There's a lot that goes behind that. So as exciting as it is and the impact

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that it's having, you know, you really got to keep your eye on the prize that

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it does take a lot of hard work to do it right and do it successfully.

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It's just the efforts are being shifted to more high value work.

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Than maybe some of the commoditized things that Sandy mentioned.

285
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Yeah, for sure. And do you think our clients are going to understand and realize that benefit sooner?

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Well, I think there's certainly a recognition that I don't think anyone is sitting

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there saying, oh, this stuff is going to be super easy.

288
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But at the same time, it's pretty clear that, look, all the stuff that you've

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had to do for decades around managing data and making sure data is of high quality,

290
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creating transparency, transparency observability all that all that great stuff

291
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that takes work and at the end of the day it's it's

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about process it's about diligence it's about

293
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accountability and ownership it's all these things that

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we talk about that just doesn't go away and so it is making sure that that's

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crystallized and that if you're talking about a ai effort right or a transformation

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for that matter all of the stuff you got to do to get your data right doesn't

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change you still got to do it now Now, how much of that can be automated?

298
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How much of that can be fast-tracked? How much can that be addressed time to

299
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market? That will certainly improve, but it certainly doesn't go away.

300
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And the effort is still pretty big. Yeah, no, it sure is.

301
00:20:20,639 --> 00:20:24,579
Mike, one of the other things that you had mentioned was the NVIDIA relationship.

302
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So that was one that we heard as an announcement.

303
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Would love to hear your reactions to that, as well as any other partnerships

304
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that had been announced during the summit.

305
00:20:34,139 --> 00:20:38,819
Yeah, I think the NVIDIA one is pretty exciting. They're obviously a juggernaut.

306
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Ever since OpenAI released ChatGPT and the world just ballooned into the AI revolution,

307
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the amount that NVIDIA has been able to do in this time period,

308
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the emphasis on their chipsets to be able to do massive amounts of computing is pretty impressive.

309
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And bringing that to the Snowflake data cloud or AI data cloud,

310
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it's incredibly important because these use cases are only going to continue

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to evolve and the amount of horsepower you have to put behind that is only going to evolve.

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So I think it's pretty exciting that there's this strategic relationship to

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try to make this as efficient.

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And cost effective as possible. And so that's sort of what I see that this relationship

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being is how do we bring the best solutions to customers and clients while minimizing

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a lot of the complexity that goes in into it?

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Because, you know, we're seeing our clients go down this path.

318
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And when they start thinking about the cost aspects and the sophistication of

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technology, there's still a big learning curve there, right?

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So I think as much as that can be sort of packaged, commingled and simplified,

321
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the better. And I think this is just a step in that direction.

322
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I think there's going to be a point of view fight between Databricks and Snowflake

323
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on what's the right approach with managing costs, because Databricks announced

324
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the ability to just forget it.

325
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Like, don't worry about it, we got it for you. And I thought that was pretty interesting.

326
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It's just like, no, you don't have it for me. I want to be able to manage this myself, right?

327
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I have to trust you now that you're using the least compute possible for my workload.

328
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And what if I want it to be faster than what you're providing to me?

329
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It's just so odd. They announced that and I cringed.

330
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I immediately cringed. It reminds me of when we were trying to sell Snowflake

331
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because all the DBAs were holding on to the fact that they could tweak the levers.

332
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And I was like, don't worry about it. It's got it for you. You just have to

333
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worry about the size of workload you need, et cetera.

334
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So it's the same feeling. I had that feeling when Databricks announced it.

335
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And I was like, that's not going to go well. And I'm not even the one building this stuff.

336
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So I don't think people will have a reaction to that.

337
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I do like the idea of not having to worry about it, but there's a trust factor there, right?

338
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You're now, you're asking your customers to trust that you have their best interests

339
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in mind, which is not always the case.

340
00:23:10,039 --> 00:23:13,439
Yeah. And it's very different, right? When you talk about feature and functionality

341
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versus, well, am I getting the value or the ROI in what I'm actually spending on, right?

342
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So when I think about feature and functionality, it's okay if it's very simplified.

343
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There can be that layer of abstraction of an understanding that,

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okay, I just need to know it works. I don't necessarily need to know everything.

345
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Science or the physics behind how it works, unless you're nerds like us,

346
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and you really want to dig into the detail.

347
00:23:40,110 --> 00:23:43,790
Well, how does that actually work in reality? But when it comes to cost,

348
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you need a high level of transparency.

349
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Think about Google BigQuery, challenges that our clients had with that in the

350
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early days of, okay, well, we're spending money, but we don't quite know what

351
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we're spending money on.

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And so I think that transparency and being able to see which departments,

353
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which divisions, which business units, you know, what are they spending? What are they using?

354
00:24:04,830 --> 00:24:10,950
I think it also having that transparency helps to drive innovation, right?

355
00:24:11,070 --> 00:24:16,010
So if you think about, well, all right, it costs X amount to do this,

356
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and we feel that's too high, make that better, right? On the feature functionality side of things.

357
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Don't try to demystify what we're spending money on because clients and CFOs

358
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and COOs, they want to understand that.

359
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CIOs want to understand that I'm getting the best value for my bucks,

360
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so to speak. And that's important.

361
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On the feature functionality side, I just feel that, hey, if it works and it

362
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works great, you're not going to get asked a lot of questions.

363
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But if you're given a number that has a currency associated with it,

364
00:24:50,070 --> 00:24:53,850
people want to know the details and they should have that level of transparency.

365
00:24:54,270 --> 00:24:58,430
Well, it doesn't just drive innovation. It drives decision-making on the business

366
00:24:58,430 --> 00:25:03,670
as well, because the business could be asking for something that costs a lot of money, right?

367
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And it could be as simple as I want that data load to happen on an hourly basis

368
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or every five minutes, all right?

369
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And that could be taxing because if I need to get it done every five minutes,

370
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maybe I need a larger compute engine to make that happen.

371
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But I'm looking at that going, well, and this has happened to us where we're

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getting into these conversations with our clients and saying,

373
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well, actually that decision is worth $10,000 a month.

374
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Is it worth to you $10,000 a month to have it seven times a day?

375
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And the answer was actually, oh God, no, it's not worth it. Right?

376
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So you can have those really pointed conversations about, I want to deliver

377
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X, it's going to cost me Y.

378
00:25:39,090 --> 00:25:43,210
Is it worth the, not just the effort to make that happen, but actually the ongoing

379
00:25:43,210 --> 00:25:45,710
costs to enable it? Yeah, exactly.

380
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Because otherwise it becomes one size fits all.

381
00:25:49,750 --> 00:25:55,110
So if you need to determine, is this delivering on the business value?

382
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If you have a one size fits all model, you may not be able to quantify that answer, right?

383
00:26:00,510 --> 00:26:03,810
But if you can break it down to your point, if you just took a very basic example

384
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and say, well, okay, if I don't have to run it as many times during the day,

385
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or I can scale back in terms of how long it runs,

386
00:26:12,290 --> 00:26:15,830
that's the optionality that people want, because they may be able to say,

387
00:26:15,930 --> 00:26:20,330
well, we need to bring the cost down in order to deliver on the ROI.

388
00:26:20,710 --> 00:26:23,050
Well, great, I have the levers to be able to do that.

389
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But if I don't, then it makes a much harder choice.

390
00:26:26,050 --> 00:26:31,150
So in some ways, you feel like you're simplifying things for people, but at the same token,

391
00:26:32,053 --> 00:26:36,433
you're creating challenges. Because at the end of the day, when you talk about

392
00:26:36,433 --> 00:26:40,753
investments and investments in technology, they're always going to be scrutinized.

393
00:26:40,953 --> 00:26:44,233
And they always have to have detailed backing behind them.

394
00:26:44,393 --> 00:26:49,093
So to me, it's almost like you want to create more transparency, not less transparency.

395
00:26:49,573 --> 00:26:53,073
For sure. For sure. And I'm really glad you brought up Databricks.

396
00:26:53,293 --> 00:27:00,593
Because if I think back to 2016, 2017, we would have these singular conversations about Snowflake.

397
00:27:00,593 --> 00:27:04,313
It was just Snowflake. And then, you know, you fast forward a couple of years

398
00:27:04,313 --> 00:27:08,613
and now you're either having a conversation about Snowflake or about Databricks,

399
00:27:08,733 --> 00:27:11,133
but never really a joint conversation.

400
00:27:11,373 --> 00:27:15,893
But I think we've seen a lot of clients starting to move to a model where they

401
00:27:15,893 --> 00:27:19,473
have both Snowflake and Databricks in their environment.

402
00:27:19,593 --> 00:27:23,213
So, you know, any thoughts on Snowflake and Databricks better together?

403
00:27:23,213 --> 00:27:27,653
Yeah, I think what's and this was one of the things that that was covered in

404
00:27:27,653 --> 00:27:31,453
the in the Snowflake conference, and I think is, is very important.

405
00:27:31,693 --> 00:27:36,333
And I'd say it's a very big takeaway, I think, for for a lot of companies is

406
00:27:36,333 --> 00:27:38,833
that you're always going to want to have optionality.

407
00:27:38,913 --> 00:27:43,513
And there's no one technology that's going to be able to solve every problem for you. Right.

408
00:27:43,633 --> 00:27:46,253
And so bring those two things together.

409
00:27:46,393 --> 00:27:53,193
And what do you have you, you need in an architecture that is not honed in,

410
00:27:53,213 --> 00:27:55,893
on any one technology, right?

411
00:27:56,053 --> 00:27:59,113
And so when you think about the Databricks or Snowflake, to me,

412
00:27:59,173 --> 00:28:02,733
it's being agnostic and taking a step back and saying, well,

413
00:28:02,833 --> 00:28:05,113
what are my objectives? What's my strategy?

414
00:28:05,493 --> 00:28:10,493
What business outcomes am I trying to drive towards? And then what does my technology

415
00:28:10,493 --> 00:28:14,113
ecosystem and architecture need to look like to support.

416
00:28:15,111 --> 00:28:17,651
And, you know, one of the things that I thought was interesting,

417
00:28:17,751 --> 00:28:22,311
and one of the partner sessions, Sandy, you and I sat in on is that Snowflake,

418
00:28:22,311 --> 00:28:26,391
in many ways, is beating that drum of making sure you have an agnostic architecture.

419
00:28:26,711 --> 00:28:33,051
And so that's not only inclusive of technology, but also the patterns that you

420
00:28:33,051 --> 00:28:34,931
deploy as part of that, right?

421
00:28:35,051 --> 00:28:38,411
Whether it's data fabric, data mesh, data lake house, data, data lake,

422
00:28:38,551 --> 00:28:42,531
I mean, the list goes on, right? And there's different reasons why you may choose

423
00:28:42,531 --> 00:28:44,051
one approach over another.

424
00:28:44,211 --> 00:28:47,951
There's different reasons why you may choose one technology over another.

425
00:28:48,151 --> 00:28:52,831
But at the end of the day, it comes down to, it's probably going to be a little

426
00:28:52,831 --> 00:28:57,611
bit of a few things that you're going to need to make sure you have modularity

427
00:28:57,611 --> 00:28:59,371
built into your overall architecture.

428
00:28:59,531 --> 00:29:03,351
And that's going to require multiple technologies at the end of the day.

429
00:29:03,471 --> 00:29:07,451
And so really what you have to look at is what is the business strategy of these

430
00:29:07,451 --> 00:29:11,351
companies And where is their secret sauce and what are they well known for?

431
00:29:11,491 --> 00:29:14,731
Right. And when they start getting out on the fringes of stuff that you sit

432
00:29:14,731 --> 00:29:19,091
there and say, OK, this is this might be a stretch or this might be an area

433
00:29:19,091 --> 00:29:22,031
of growth that maybe doesn't make a lot of sense.

434
00:29:22,231 --> 00:29:25,411
Well, then you may want to stick with best of breed in that case.

435
00:29:25,411 --> 00:29:28,991
So, you know, I think it's important of balancing those things,

436
00:29:29,031 --> 00:29:34,951
of continue to be agnostic and knowing that there needs to be an overall ecosystem

437
00:29:34,951 --> 00:29:39,391
of technologies that have to, you know, have interplay with each other.

438
00:29:39,511 --> 00:29:44,091
And to me, it's not just going to be Snowflake or Databricks, one or the other, both.

439
00:29:44,331 --> 00:29:48,771
You know, I think it really depends on the situation and the client and their

440
00:29:48,771 --> 00:29:52,351
investments and their strategy, all the things that I mentioned.

441
00:29:52,491 --> 00:29:55,771
And I think we'll see that evolve over time. It was going to be acquisitions

442
00:29:55,771 --> 00:29:59,811
in the market and a lot of these AI companies are going to become part of bigger companies.

443
00:30:00,991 --> 00:30:04,991
So I'd say in the next five years, it'll be interesting to see how it all plays out.

444
00:30:05,659 --> 00:30:09,359
At the end of the day, it's what is your product strategy? Where are you taking this?

445
00:30:09,599 --> 00:30:13,339
And I do think Snowflake's on the right track. They're not trying to break out

446
00:30:13,339 --> 00:30:16,939
into areas that just are far removed from what they do.

447
00:30:17,179 --> 00:30:21,039
They're creating that ecosystem around them to be able to do that,

448
00:30:21,139 --> 00:30:23,239
and they're staying true to the core.

449
00:30:23,499 --> 00:30:27,759
And that's important, I think, because once you start deviating from your core

450
00:30:27,759 --> 00:30:29,999
strategy, that's when things get messy.

451
00:30:30,339 --> 00:30:34,899
And when you start seeing lots Lots of acquisitions and then stuff starts to fall apart.

452
00:30:35,099 --> 00:30:39,999
It wasn't as great as it used to be. And that's just, it's sort of the evolution of growth in many ways.

453
00:30:40,199 --> 00:30:42,899
But it'll be interesting to see over the next five years. Absolutely.

454
00:30:43,019 --> 00:30:48,379
I think what's been interesting is the whole true decoupling of data from the compute, right?

455
00:30:48,479 --> 00:30:51,799
Because now with Iceberg, because Databricks bought Tabular,

456
00:30:51,819 --> 00:30:55,299
Tabular allows them to use anything, including Iceberg and other formats. mats.

457
00:30:55,579 --> 00:31:02,399
And Snowflake is making this huge iceberg push to be able to work on data sitting on iceberg.

458
00:31:02,739 --> 00:31:07,959
So I think it's just going to be a matter of a rush of who's actually able to

459
00:31:07,959 --> 00:31:11,779
work on data and compute with data faster,

460
00:31:11,939 --> 00:31:16,499
and who has the most tools and the tool belt to enable you to do that.

461
00:31:16,599 --> 00:31:18,299
Is it going to be Databricks? Is it going to be Snowflake?

462
00:31:18,419 --> 00:31:23,039
It could be both. Your data could be in one place and both these platforms can now work on it.

463
00:31:23,099 --> 00:31:28,179
So that's like a completely different thing that I can literally put it in this like other thing.

464
00:31:28,439 --> 00:31:32,639
And then both these platforms can actually do workloads on that data set.

465
00:31:33,008 --> 00:31:36,748
And not move it. That changes the game. And even Salesforce got in on it, right?

466
00:31:36,848 --> 00:31:40,488
So Salesforce now can load data in and out of Iceberg as well,

467
00:31:40,508 --> 00:31:42,428
or read data in and out of Iceberg as well.

468
00:31:42,528 --> 00:31:46,748
So this whole interoperability of where your data could be sitting in this one

469
00:31:46,748 --> 00:31:51,608
thing and all these other applications and platforms can work on it, touch it, read it.

470
00:31:51,728 --> 00:31:53,928
I think that's a whole new world.

471
00:31:54,108 --> 00:31:56,988
That's a great point. Totally agree. So with all that being said,

472
00:31:57,188 --> 00:32:02,308
were there any Any even better if announcements that you were looking for?

473
00:32:02,408 --> 00:32:04,528
Maybe a little bit more information, a little bit more clarity?

474
00:32:04,808 --> 00:32:08,268
Yeah, I kind of come back to my earlier point. I think some of the things that

475
00:32:08,268 --> 00:32:13,288
wanting to see come out of private preview be generally available would probably

476
00:32:13,288 --> 00:32:18,028
want harder timeframes of when that's going to happen. So I'd say that was one of the takeaways.

477
00:32:18,408 --> 00:32:24,188
I think the other thing is you always want to see customer cases and customer stories, right? Right.

478
00:32:24,228 --> 00:32:28,508
For me, going to these conferences, it's less about understanding the nuts and

479
00:32:28,508 --> 00:32:32,588
bolts of the technology, but more what's the applicability of that technology

480
00:32:32,588 --> 00:32:35,448
to a business use case. Right.

481
00:32:35,568 --> 00:32:39,508
How is this actually going to help move the needle for for one of my clients?

482
00:32:39,688 --> 00:32:42,788
But definitely want to see where people are being successful,

483
00:32:42,948 --> 00:32:48,808
but then also being transparent and saying, hey, we're not being so successful here. and here's why.

484
00:32:48,948 --> 00:32:51,128
And these are some of the things that we've tried. Because again,

485
00:32:51,208 --> 00:32:56,068
more information and more eyeballs and more voices in this just makes it all

486
00:32:56,068 --> 00:32:57,228
the better for everybody else.

487
00:32:57,508 --> 00:33:03,028
Were there any use cases that maybe are different than what we've seen with

488
00:33:03,028 --> 00:33:05,808
our clients and maybe something that we should be considering?

489
00:33:06,088 --> 00:33:07,528
I'd say just the contrary.

490
00:33:07,908 --> 00:33:13,048
I mean, it was good to validate that a lot of the things that our clients are

491
00:33:13,048 --> 00:33:17,428
struggling with and the solutions that we're bringing to bear for them.

492
00:33:17,508 --> 00:33:22,368
You were seeing other customers of Snowflake in the same camp and trying to

493
00:33:22,368 --> 00:33:24,568
address maybe in different ways or nuanced ways.

494
00:33:24,748 --> 00:33:29,508
But at the end of the day, I think the same client problems and themes are consistent.

495
00:33:29,768 --> 00:33:33,608
And so I think in many ways, it was just a validation of a lot of the great

496
00:33:33,608 --> 00:33:36,368
work that our teams are doing to support our clients.

497
00:33:36,861 --> 00:33:40,621
Good. So our clients' challenges are not each special snowflakes.

498
00:33:40,901 --> 00:33:43,501
That was such a terrible pun.

499
00:33:44,221 --> 00:33:48,461
I mean, I didn't see anything crazy and outlandish. I actually agree with Mike.

500
00:33:48,541 --> 00:33:52,181
I think what I saw, and most of it was from Snowflake, like the art of the possible

501
00:33:52,181 --> 00:33:54,761
with these challenges that we've seen clients have time and time again.

502
00:33:54,941 --> 00:33:57,981
Art of the possible with all the new capabilities that they're bringing to there.

503
00:33:57,981 --> 00:34:02,021
That was what I was noticing was, okay, there's a different way to solve this,

504
00:34:02,081 --> 00:34:06,621
that they're pitching to their partners and customers, because obviously no

505
00:34:06,621 --> 00:34:09,901
one's doing it quite yet. They haven't released a lot of this stuff.

506
00:34:10,041 --> 00:34:13,581
So it was very much pie in the sky conversations about where it was headed.

507
00:34:13,581 --> 00:34:17,941
But I felt there was a shift in terms of, I don't have to ask the question anymore

508
00:34:17,941 --> 00:34:21,881
of, can I do something? Like, I think you can do a lot now.

509
00:34:22,361 --> 00:34:26,141
I think the question now is what should I be doing? And that's something that

510
00:34:26,141 --> 00:34:28,661
we do very uniquely with our clients.

511
00:34:29,501 --> 00:34:33,921
I think a lot of people wait to hear from their clients in terms of what problems

512
00:34:33,921 --> 00:34:34,721
they're trying to solve.

513
00:34:34,901 --> 00:34:39,001
And we kind of are front footed in terms of helping our clients figure out what

514
00:34:39,001 --> 00:34:42,301
are the right problems to solve And then how to solve them becomes secondary.

515
00:34:42,541 --> 00:34:47,621
So that, to me, was exciting because it allows us to have more freedom in terms

516
00:34:47,621 --> 00:34:52,061
of the advice that we're giving and the propositions that we're discussing with

517
00:34:52,061 --> 00:34:55,621
them. Yeah, I think that's a great point. I mean, I see a couple things.

518
00:34:55,821 --> 00:35:00,121
So one is, I think the art of the possible, I think there's a recognition on,

519
00:35:00,201 --> 00:35:03,801
yes, a lot of these things are real and quite possible.

520
00:35:03,961 --> 00:35:09,801
I do agree that there's a bit of what are the use cases we should be going after

521
00:35:09,801 --> 00:35:12,421
and where can we get the most pull through.

522
00:35:12,421 --> 00:35:16,361
I think there's going to be continued on the art of the possible side of it,

523
00:35:16,381 --> 00:35:21,541
of just understanding and learning of the applicability of AI and gen AI.

524
00:35:21,781 --> 00:35:24,621
I mean, it's not like these things are new, right? They're just,

525
00:35:24,661 --> 00:35:26,761
they've evolved very quickly.

526
00:35:26,881 --> 00:35:30,161
And that's what's opened up the aperture, I think, for many companies.

527
00:35:30,361 --> 00:35:32,401
But this has been around for decades, right? Right.

528
00:35:32,621 --> 00:35:38,041
So it's just now it's just supercharged and it's it's a lot more realistic for

529
00:35:38,041 --> 00:35:39,701
companies to be able to do this at scale.

530
00:35:39,801 --> 00:35:43,901
But I think there's a big learning curve that comes with that in a number of

531
00:35:43,901 --> 00:35:46,441
different areas. Right. We talk about responsible AI.

532
00:35:46,581 --> 00:35:49,741
We talk about high quality data. We talk about hallucination.

533
00:35:49,801 --> 00:35:53,781
We talk about all these different things. Well, people have to learn what does that all mean?

534
00:35:54,533 --> 00:35:58,473
And how do I make sure that I'm addressing all of those things on equal footing, right?

535
00:35:58,573 --> 00:36:02,393
That I'm not letting one thing to chance that could really have,

536
00:36:02,393 --> 00:36:04,773
you know, bad implications on your company.

537
00:36:04,913 --> 00:36:08,713
And so there's, there's going to be this continued learning that that goes along

538
00:36:08,713 --> 00:36:13,713
with that being true to understanding where you are in that journey, right?

539
00:36:13,833 --> 00:36:17,413
And what you're capable of doing, you know, short term and long term,

540
00:36:17,473 --> 00:36:18,773
right? And how you build up to that.

541
00:36:18,893 --> 00:36:24,873
And what are the use cases we should go after? or will they create the value that they're meant to?

542
00:36:25,173 --> 00:36:29,193
You know, you sort of have to get past this building proof of concepts and prototypes.

543
00:36:29,653 --> 00:36:33,733
And so I think you have to be realistic about what companies can do and what

544
00:36:33,733 --> 00:36:34,993
they can evolve to over time.

545
00:36:35,233 --> 00:36:38,493
What I'm seeing is that a lot of clients, because a lot of these capabilities

546
00:36:38,493 --> 00:36:42,273
are now sort of real and evident and right in front of them,

547
00:36:42,393 --> 00:36:44,513
you know, they're off building a lot of different things.

548
00:36:44,613 --> 00:36:47,113
And so it's almost like you got to take a step back and say,

549
00:36:47,193 --> 00:36:49,053
all right, you need to see the forest or the trees.

550
00:36:49,133 --> 00:36:52,133
Are these investments you're making and some of these short-term things,

551
00:36:52,233 --> 00:36:55,933
because you can do it, are they the right investments to be making right now?

552
00:36:56,053 --> 00:36:59,733
Because what you may build today, where there may not be a solution,

553
00:36:59,893 --> 00:37:04,333
just a lot of capability to do it, well, those things could just become available

554
00:37:04,333 --> 00:37:06,213
in the next year or two, right?

555
00:37:06,333 --> 00:37:09,773
So you have to balance the investments that companies are making and building

556
00:37:09,773 --> 00:37:15,313
things bespoke versus things that are going to start being built into different

557
00:37:15,313 --> 00:37:17,193
software, different platforms.

558
00:37:17,693 --> 00:37:22,613
Different maybe open source capabilities, you really have to look at that and

559
00:37:22,613 --> 00:37:24,093
have a view of the future.

560
00:37:24,213 --> 00:37:27,773
Because case in point, a client came up to us recently, and they said,

561
00:37:27,833 --> 00:37:29,953
hey, we want to take this beyond POC, we want to build this.

562
00:37:30,053 --> 00:37:34,453
And I said, well, maybe one of the things we should first do is is just do a

563
00:37:34,453 --> 00:37:40,053
quick scan of well, what's happened in the last six months since you started

564
00:37:40,053 --> 00:37:44,573
on this journey, because there is there's a lot more advancement that's happened.

565
00:37:44,793 --> 00:37:47,713
And as a result of that, there's sort of a recognition, okay,

566
00:37:47,813 --> 00:37:50,793
it probably doesn't make sense for us to build this at this time.

567
00:37:50,873 --> 00:37:54,973
We can build a portion of it, but we know that there's some other things coming soon.

568
00:37:55,153 --> 00:37:59,053
And so let's treat it like an evolution, right? Let's build the things that

569
00:37:59,053 --> 00:38:03,273
are going to differentiate, but let's not build things that aren't going to

570
00:38:03,273 --> 00:38:07,413
give us the true value proposition that we're looking for.

571
00:38:07,573 --> 00:38:12,933
I think of two things based on what you just said. One is tech debt velocity

572
00:38:12,933 --> 00:38:18,353
is at an all-time high around this like nobody else ever in the past because

573
00:38:18,353 --> 00:38:22,073
not only are we building things that within a few months become obsolete.

574
00:38:22,791 --> 00:38:27,411
That is at a higher level than we've ever seen before, like ever.

575
00:38:27,551 --> 00:38:32,291
And this is not just in AI specifically, I would say, Gen AI specifically.

576
00:38:32,511 --> 00:38:37,271
And then number two is really making that call of what do I wait?

577
00:38:37,491 --> 00:38:42,171
What is the right time to wait on this? Because it is moving so quickly.

578
00:38:42,331 --> 00:38:46,931
You really have to think about that and have that optionality that we always used to push for.

579
00:38:47,011 --> 00:38:50,651
You have to have that optionality as you build and model these things out because

580
00:38:50,651 --> 00:38:54,331
we're moving so quickly. So minimize the tech debt, create it in a modular fashion,

581
00:38:54,491 --> 00:38:56,031
think about the optionality around this.

582
00:38:56,111 --> 00:39:00,991
Otherwise, you're going to be paying for it three times over. A hundred percent.

583
00:39:01,071 --> 00:39:04,551
And seeing that, because I look at client roadmaps, right?

584
00:39:04,691 --> 00:39:08,971
And you talk about the sequencing of certain things and you talk about which

585
00:39:08,971 --> 00:39:11,591
ones that they're going off and building.

586
00:39:11,871 --> 00:39:16,831
And it's like, did you know that there's this technology is coming out in the next six months?

587
00:39:16,891 --> 00:39:20,131
You may want to think about reprioritization of your roadmap.

588
00:39:20,131 --> 00:39:24,751
And so I think this continual checkup that you do around your data and your

589
00:39:24,751 --> 00:39:27,551
AI strategy is going to be incredibly important.

590
00:39:27,731 --> 00:39:30,871
It's not, hey, let's build a three-year strategy and roadmap.

591
00:39:31,231 --> 00:39:34,551
Set it and forget it and stay on that journey for the next three years,

592
00:39:34,611 --> 00:39:35,751
and then we'll revisit it.

593
00:39:35,831 --> 00:39:38,271
It's like, no, you should probably be revisiting this quarterly.

594
00:39:38,531 --> 00:39:43,351
Because every quarter, you may be in a situation where you might want to reprioritize

595
00:39:43,351 --> 00:39:47,011
certain things based on what's happened in the last 90 days,

596
00:39:47,231 --> 00:39:49,511
because it is moving that fast. Yeah.

597
00:39:49,771 --> 00:39:54,831
I mean, who would have thought we would be talking about revisiting roadmaps every 90 days?

598
00:39:55,251 --> 00:39:59,971
We create them so we can follow them for the period that we created them for,

599
00:40:00,071 --> 00:40:02,371
which is typically two to three years.

600
00:40:02,651 --> 00:40:07,711
So to say, let's revisit in 90 days, I don't know, it's a little different than

601
00:40:07,711 --> 00:40:13,491
what we're used to. Well, I don't think it's as much about revisiting the business objectives.

602
00:40:14,111 --> 00:40:18,991
I mean, you're still moving to that North Star, right?

603
00:40:19,271 --> 00:40:23,771
It's more of just, are we on the right track? Are we incurring debt?

604
00:40:23,971 --> 00:40:28,111
I mean, if you talk about Agile and the whole point of why Agile sort of took

605
00:40:28,111 --> 00:40:32,411
over Waterfall, and when you talk about technical delivery, it's because you

606
00:40:32,411 --> 00:40:35,731
can get ahead of the issues much quicker, right?

607
00:40:35,731 --> 00:40:40,071
Right. You don't want to wait till you're in UAT to realize we completely missed

608
00:40:40,071 --> 00:40:41,071
the mark for the business.

609
00:40:41,251 --> 00:40:46,071
And so the last year we've spent on this project, we're now behind the eight

610
00:40:46,071 --> 00:40:48,291
ball. Nobody wants to get to that point. Right.

611
00:40:48,511 --> 00:40:52,271
And so to me, it's no different. Right. It's almost like Agile where it's like,

612
00:40:52,371 --> 00:40:53,151
hey, we want to do retrospectives.

613
00:40:53,791 --> 00:40:57,451
We want to understand what's working, what's not working and reassess.

614
00:40:57,531 --> 00:41:00,551
You know, so it's not about like constant replanning. It's just more.

615
00:41:00,631 --> 00:41:01,751
Are we on the right track?

616
00:41:02,011 --> 00:41:05,831
Are we being sensible? And so it's just having that checkup and making sure

617
00:41:05,831 --> 00:41:09,631
that, yep, we feel good. You know, nope, we don't feel good.

618
00:41:09,791 --> 00:41:12,231
Here's why. Okay, let's reassess.

619
00:41:12,601 --> 00:41:15,621
But the business objectives, you're still moving towards that.

620
00:41:15,921 --> 00:41:20,021
As I said, data work is hard. Data work is going to continue to be hard, but in different ways.

621
00:41:20,161 --> 00:41:25,681
Yeah. Yeah. I mean, there's no easy solution to saying, I want my data perfect, right?

622
00:41:25,801 --> 00:41:29,121
Yeah, exactly. I want it 100% accurate. I want it perfect.

623
00:41:29,541 --> 00:41:32,981
The technology advancements are there, but it's a discipline.

624
00:41:33,221 --> 00:41:36,981
I mean, we all know that. That's why we say, do the hard work on governance.

625
00:41:37,221 --> 00:41:41,661
It's because it's not easy, right? And if you really want those things,

626
00:41:41,841 --> 00:41:44,341
that requires human beings to be accountable.

627
00:41:44,521 --> 00:41:49,121
And that's what it takes. And that hasn't changed in however many years.

628
00:41:49,521 --> 00:41:52,901
AI will help it. It's not going to change it. And in some ways,

629
00:41:52,941 --> 00:41:57,041
it will create more challenges, which we've already seen. But it's exciting times.

630
00:41:57,401 --> 00:42:03,321
Snowflake aside, just that tagline of being able to see clients evolve with

631
00:42:03,321 --> 00:42:08,321
data, with the right tools, with the right capabilities, with the right strategies.

632
00:42:08,321 --> 00:42:12,461
It's going to be an interesting few years ahead of us. Yeah, it's exciting.

633
00:42:12,701 --> 00:42:15,941
Yeah, totally. I'm personally very, very excited about this.

634
00:42:16,221 --> 00:42:21,041
Mike, thank you so much for taking the time to chat about Snowflake Summit with us.

635
00:42:21,261 --> 00:42:25,021
My pleasure. It's very enlightening. I learned a lot. I hope everybody else

636
00:42:25,021 --> 00:42:26,441
that's listening did as well.

637
00:42:26,621 --> 00:42:30,261
I love that I just get to get listen to Mike geek out for a little bit.

638
00:42:32,121 --> 00:42:36,761
It's always fun. Yeah, it's nice to take a step back and sort of reassess all

639
00:42:36,761 --> 00:42:41,061
the information overload that you get from talking to partners, talking to clients,

640
00:42:41,441 --> 00:42:45,881
listening to the keynotes, listening to all the different sessions to take a

641
00:42:45,881 --> 00:42:49,141
step back and say, okay, what does this mean in the grand scheme?

642
00:42:49,281 --> 00:42:52,581
I think is important and love the podcast.

643
00:42:52,921 --> 00:42:57,121
Appreciate you having me on and look forward to when I get to come on again

644
00:42:57,121 --> 00:42:59,001
and whatever that topic's going to be.

645
00:42:59,581 --> 00:43:03,821
We'll figure that one out soon. All right. Well, thank you. Thank you, Paul. Appreciate it.

646
00:43:06,561 --> 00:43:07,501
You.