WEBVTT

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<v Speaker 1>Welcome back to dot net rocks. We're from Build.

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<v Speaker 2>Yeah, Richard, I like being a build Well, Seattle, it's raining.

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<v Speaker 2>Welcome to my neck of the woods.

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<v Speaker 1>Right, I'm Carl Franklin. That's Richard Campbell, and it's gonna

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<v Speaker 1>be a good show. This is the first show that

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<v Speaker 1>we published from the list of shows that we're doing.

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<v Speaker 2>It well, got us to be busy.

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<v Speaker 1>Yes, however, this is the second show we recorded. Okay,

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<v Speaker 1>so the keynote uh Hawk that we do with Scott

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<v Speaker 1>Hunters next week. Yeah, yeah, all right, it's a couple

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<v Speaker 1>hours ago, a couple hours ago, a lot of good stuff.

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<v Speaker 1>Ken Exner is here. We're gonna be talking to him

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<v Speaker 1>in just a minute. But first let's roll the music

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<v Speaker 1>for better know a framework.

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<v Speaker 2>Man, what do you got?

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<v Speaker 1>Well? I haven't seen this one before. But it's trending

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<v Speaker 1>on githubs. This is public dash APIs GitHub. It's the

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<v Speaker 1>public API repository, manually curated by community members like you

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<v Speaker 1>and folks working at API Layer. It's an extensive list

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<v Speaker 1>of public APIs for many domains you can use for

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<v Speaker 1>your own products. Consider it a treasure trove of api

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<v Speaker 1>is well managed by the community over the years. That's cool,

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<v Speaker 1>you know, so putting on my career criminal advice hat.

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<v Speaker 1>If you want to do a denial of service attack

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<v Speaker 1>to test out your new tools, pick one of these

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<v Speaker 1>APIs and let it rip.

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<v Speaker 2>Jeezus, that's dark. It's a very dark way to think

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<v Speaker 2>about it.

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<v Speaker 1>But yeah, I know, of course, you know you want

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<v Speaker 1>to test out your your new harness for calling APIs

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<v Speaker 1>and see if they were There's some really good stuff here.

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<v Speaker 1>Locate and identify website visitors by IP address. That's a

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<v Speaker 1>fun one market stack, free, easy to use rest API

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<v Speaker 1>interface delivering worldwide stock market data and Jason format because

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<v Speaker 1>you know CNN doesn't work well enough. Weather Stack retrieve

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<v Speaker 1>incedant accurate weather information, numb verify, global phone number validation,

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<v Speaker 1>look up Jason API and Fixer. A simple and lightweight

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<v Speaker 1>API for current and historical foreign exchange rates, some good stuff,

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<v Speaker 1>and there's more to it, of course, but that's it awesome.

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<v Speaker 1>That's what I got. Who's talking to us today, Richard grab.

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<v Speaker 2>Your comment of the show ten sixteen seven, which is

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<v Speaker 2>a while ago. Let's tart twenty fourteen. We were talking

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<v Speaker 2>about to edemar a bit about different search strategies and technologies,

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<v Speaker 2>and one of the comments in the show came from

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<v Speaker 2>Sean mccartall, who was talking about how he pitched a

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<v Speaker 2>last search to his organization. And admittedly he's twenty fourteen,

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<v Speaker 2>so think about the challenges back then, right, But he said,

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<v Speaker 2>only after the second time I had to tell my

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<v Speaker 2>boss I don't know why it broke. We don't have

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<v Speaker 2>enough information I sought out to solve the problem. Yeah,

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<v Speaker 2>so this is this is an EMEN type thinking, he said,

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<v Speaker 2>And the problem this is an architecture that spans SharePoint, SAP,

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<v Speaker 2>some Tomcat hosted solutions, so a little jab in there,

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<v Speaker 2>along with a bunch of good dot net projects in too,

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<v Speaker 2>and he worked with Python and power Show forty threw

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<v Speaker 2>a Lodge staff receiver into a LASTI search cluster, which

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<v Speaker 2>was the easiest part to set up. And then after

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<v Speaker 2>by another week doing some tweaks and configurations, he was

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<v Speaker 2>able to do an analysis of workflows through all of

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<v Speaker 2>those different products in twenty fourteen. That's twenty fourteen. I

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<v Speaker 2>mean today the tools are all different. You'd be in

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<v Speaker 2>the cloud and doing that, but no, he was cracking

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<v Speaker 2>it the hard way now. So now I appreciate that

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<v Speaker 2>it's just a realization, like a last search bit in

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<v Speaker 2>our lives a long time. Things are obviously different now.

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<v Speaker 2>And as Sean, thank you so much for your comment,

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<v Speaker 2>and a copy of music Coby is on its way

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<v Speaker 2>to you. And if you'd like a copy of music Koba,

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<v Speaker 2>I write a comment on the website at dot net

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<v Speaker 2>rocks dot com or on the facebooks we've published every

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<v Speaker 2>show there, and if you comment there and never real

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<v Speaker 2>on the show, we'll send you copy of music Goby.

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<v Speaker 1>Music to Code by of course, the music that helps

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<v Speaker 1>you focus, still going strong, twenty two tracks. You can

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<v Speaker 1>get it at music toocodebuy dot net. You can get

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<v Speaker 1>the collection in MP three wave or a flack format.

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<v Speaker 1>Before we interview our guests and introduce him, let's talk

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<v Speaker 1>about nineteen fifty two yep so significant events, because this

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<v Speaker 1>is show nineteen fifty two kind of have been been

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<v Speaker 1>doing this. What happened in that year significant events including

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<v Speaker 1>the death of King George the sixth and the ascension

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<v Speaker 1>of his daughter Elizabeth to the throne of the United Kingdom.

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<v Speaker 1>The US successfully tested the first hydrogen bomb. Dwight D.

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<v Speaker 1>Eisenhower was elected President of the United States, defeating Adale Stevenson.

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<v Speaker 1>The Korean War continue with the US launching bombing attacks

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<v Speaker 1>in Greece and Turkey joint NATO. Other key events include

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<v Speaker 1>the beginning of the Mau Mau rebellion in Kenya and

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<v Speaker 1>the Great Smog of London caused by industrial pollution and

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<v Speaker 1>high pressure weather. The Great Smog not to be confused

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<v Speaker 1>with the Great Stink. Do you remember that? I mean

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<v Speaker 1>I don't remember it, but I remember hearing about it.

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<v Speaker 1>Sewage backed up in the Thames. It was horrible.

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<v Speaker 2>Related to the presidential election nineteen fifty two, was one

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<v Speaker 2>of the UNIVAC computers, which again these were all bespoke

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<v Speaker 2>machines of the time, right, actually did the computational estimations

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<v Speaker 2>to say that Eisenhower's going to win. So it's one

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<v Speaker 2>of the first times the computer predicted the win based

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<v Speaker 2>on poll.

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<v Speaker 1>Okay, so you know how many times did the computer

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<v Speaker 1>not predict the win though?

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<v Speaker 2>Well before that?

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<v Speaker 1>Zero? Oh yeah, okay, yeah, right, okay, shall we get

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<v Speaker 1>on with our show. Let's talk to Ken Xner. I'll

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<v Speaker 1>introduce him here. Ken is chief product Officer, head of

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<v Speaker 1>products and engineering for Elastic. Maybe you've heard of it.

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<v Speaker 3>Welcome Ken, Hey Carl, good to be here.

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<v Speaker 2>Hey Richard, thanks, good to have you. Yeah, thank you.

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<v Speaker 3>You mentioned uh Elastic search US in twenty fourteen. We

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<v Speaker 3>actually just celebrated our fifteenth anniversary of.

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<v Speaker 2>The lasted search, so twenty ten.

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<v Speaker 3>Yeah, Elastic Search fifteen years as a couple of months ago.

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<v Speaker 1>That's fantastic.

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<v Speaker 3>H And it's a it's gone on to be downloaded

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<v Speaker 3>four point six billion times.

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<v Speaker 1>Wait billion with a B, billion with a B, which.

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<v Speaker 3>Makes it I think one of the most popular open

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<v Speaker 3>source projects of all time.

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<v Speaker 2>Yeah, no, without doubt, And I mean it comes up regularly.

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<v Speaker 2>It's just one of the tools we've count on for

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<v Speaker 2>fast search solving certain certain scopes of problems. Right, see

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<v Speaker 2>the smart casing tool. Now, you guys have got a

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<v Speaker 2>new relationship in Azure. You're just you're becoming a service

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<v Speaker 2>like well, how you guy, what do you guys are

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<v Speaker 2>up to?

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<v Speaker 1>But we've we've.

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<v Speaker 3>Been Our partnership with Azure has been fantastic. So over

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<v Speaker 3>the last few years, Azure has been integrating US almost

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<v Speaker 3>like a first party service within Azure, so you can

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<v Speaker 3>go into the navigation and select US as part of

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<v Speaker 3>the experience with an Azure So if you wanted to

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<v Speaker 3>launch your own Elastic Search, you can do it very

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<v Speaker 3>simply from within the Azure console. So yeah, it's been fantastic.

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<v Speaker 2>Yeah, and you guys been in cloud for ages.

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<v Speaker 3>We started going back to the cloud, started going to

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<v Speaker 3>cloud about six seven years ago, where we first started

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<v Speaker 3>letting customers spin up a VM, have US automatically installed,

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<v Speaker 3>have US patching and maintaining their cluster. We've recently actually

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<v Speaker 3>introduced a new serverless offering that is kind of a

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<v Speaker 3>fully managed experience that you can think of it as

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<v Speaker 3>SaaS like. So it is a SaaS like experience for

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<v Speaker 3>using Elastic Search, which means that customers can use US

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<v Speaker 3>an open source, they can get a self managed license,

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<v Speaker 3>they can have a hosted version in cloud, and they

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<v Speaker 3>can have this new serverless offering which is kind of

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<v Speaker 3>a SaaS like experience.

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<v Speaker 2>So am I just paying by transaction at that point?

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<v Speaker 3>Yeah, you're just paying for data. So if you're if

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<v Speaker 3>you're using US as like a logging solution, you're just

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<v Speaker 3>paying for data in and data retained.

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<v Speaker 1>That's it.

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<v Speaker 2>That's fair.

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<v Speaker 3>And it scales to zero and it's uh yeah, it's

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<v Speaker 3>a magical.

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<v Speaker 2>It doesn't cost you anything. You don't move any data

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<v Speaker 2>around it exactly nice. You're if you're running a VM,

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<v Speaker 2>you're paying for it. That's right, no way around that.

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<v Speaker 3>But yeah, it's it's been a challenge being able to

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<v Speaker 3>deliver elastic search all these different ways that you know,

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<v Speaker 3>it's hard to maintain software that many different ways, but

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<v Speaker 3>it's what customers need.

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<v Speaker 1>So how does it fit into Azure's new AI focus

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<v Speaker 1>that you probably, yeah, are the focus this I did.

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<v Speaker 2>The drinking game I decided on was not to say

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<v Speaker 2>we need to take a drink, we say copa certainly

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<v Speaker 2>not say drinks, we say AI, you'll be drunk when

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<v Speaker 2>you say Windows.

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<v Speaker 1>Yeah.

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<v Speaker 2>I think I only had to take three drinks then.

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<v Speaker 3>Yeah. Well, I think it's really a question of how

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<v Speaker 3>is search, you know, playing a role in the I

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<v Speaker 3>right boom, And I think one of the things that

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<v Speaker 3>you know, when generative the I first happened, everyone's kind

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<v Speaker 3>of wondering what's the future of search, and people were saying, oh,

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<v Speaker 3>you know, Google is dead, and I think one of.

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<v Speaker 1>The things that you even said, SAS is dead.

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<v Speaker 3>He said that last year.

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<v Speaker 2>Yeah.

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<v Speaker 4>Yeah, yeah, I'm saying for him to say, I think

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<v Speaker 4>it seems unlikely, maybe a little premature, maybe a little

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<v Speaker 4>we'll get into agentic architectures in the augentic future soon,

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<v Speaker 4>but yeah, I.

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<v Speaker 3>Think SaaS is still here. But yeah, what we realized

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<v Speaker 3>is that search had a huge role to play in

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<v Speaker 3>AI because if you're building generative AI applications, you need

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<v Speaker 3>to ground it on, you know, your own data so

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<v Speaker 3>that you're not just basing on public information.

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<v Speaker 2>And constantly changing.

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<v Speaker 3>It's constantly changing. And if you, you know, want to

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<v Speaker 3>make sure that you know the permissions reflect the user,

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<v Speaker 3>you need to make sure that you know that whatever

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<v Speaker 3>you're grounding on understands the user's permission to data.

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<v Speaker 2>You're not going to build a model for every permission level.

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<v Speaker 2>That doesn't seem practical.

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<v Speaker 3>You know, fine tune or build a model for every

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<v Speaker 3>single possible combination combination of permissions.

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<v Speaker 2>Yeah, yeah, it seems like a way, good way to

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<v Speaker 2>waste a lot of space.

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<v Speaker 1>What else shouldn't you do let's talk about that.

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<v Speaker 3>Well maybe in video would like that feature, but.

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<v Speaker 2>Don't stick a fork at a toaster. That's a good one,

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<v Speaker 2>I think.

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<v Speaker 1>Okay, then we have two.

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<v Speaker 3>Yeah, so search has this new found life in the

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<v Speaker 3>age of generative AI. Wheople are using us to help

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<v Speaker 3>build applications and doing retrieval mechanisms, and that's been sort

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<v Speaker 3>of the source of our partnership with Azure, as well

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<v Speaker 3>as helping them be a search engine or retrieval engine

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<v Speaker 3>that helps their customers, our customers build these generative applications

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<v Speaker 3>on top of their private data.

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<v Speaker 1>Right, Yeah, that's great. Well, I mean we saw the

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<v Speaker 1>we just saw the keynote a couple hours ago, and

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<v Speaker 1>we'll get into more of that next week with Scott.

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<v Speaker 1>But what did you take away from it?

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<v Speaker 3>Well, one of the things in Scott's keynote he mentioned

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<v Speaker 3>sort of the move towards conversational search, and this is

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<v Speaker 3>one that we think is big within Elastic and we've

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<v Speaker 3>seen the evolution of search. You know, started off as

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<v Speaker 3>lexical search or text basse search, and then six seven

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<v Speaker 3>years ago and moved to semantic search, where people started

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<v Speaker 3>to expect their search box to understand natural language and

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<v Speaker 3>respond to questions. And then I think with generative AI

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<v Speaker 3>it became sort of an evolution further towards RAG where

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<v Speaker 3>people wanted to build these applications. I think now it's

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<v Speaker 3>moving towards conversational search, where you're expecting your search box

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<v Speaker 3>to have a conversation with you and respond in natural

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<v Speaker 3>language and carry the context and carry context, have memory

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<v Speaker 3>be personalized. And I think one of the things that

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<v Speaker 3>we've been working with Microsoft on is how do you

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<v Speaker 3>build these conversational search experiences across the web. I'm excited

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<v Speaker 3>about that.

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<v Speaker 2>Yeah, yeah, and again it's just it's part of the infrastructure.

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<v Speaker 2>You just want you just expect that to work and

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<v Speaker 2>to be fast, to be fast.

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<v Speaker 3>Yeah, you know, that's one of the things that elastic

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<v Speaker 3>search is known for is blazing speed. I'm always you

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<v Speaker 3>can you start typing keywords in a search box and

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<v Speaker 3>auto completes. You expect that. So now combine that with

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<v Speaker 3>like conversational search, you can.

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<v Speaker 2>Get over the speed of l about the typing in

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<v Speaker 2>the box, yeah, yeah, if I have to finish typing

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<v Speaker 2>and hit a box, so annoying. That's a broken search tool.

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<v Speaker 1>Yeah, so many of our listeners may not know about

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<v Speaker 1>vector databases in general. Can you kind of give us

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<v Speaker 1>an idea of how that changes the game sure data

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<v Speaker 1>standard relationship.

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<v Speaker 3>Yah. So when we became a semantic search engine, we

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<v Speaker 3>had to become a vector database as well. And this

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<v Speaker 3>is back around twenty seventeen we started supporting the ability

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<v Speaker 3>to store and query dence spectors.

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<v Speaker 2>It was cool before it was cool, five years before.

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<v Speaker 3>Before it was cool.

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<v Speaker 1>I don't know anything about that. It's like first podcast ever.

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<v Speaker 3>I'm there's some debate about it, but I'm gonna still

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<v Speaker 3>argue that we were one of, if not the first

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<v Speaker 3>vector database out there because we had to support it

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<v Speaker 3>for product search like image search, and for semantic search.

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<v Speaker 1>So I kind of know what vectors are because I

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<v Speaker 1>took high school physics. But tell us the.

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<v Speaker 3>Basic idea is, you're trying to plot vettings or vectors

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<v Speaker 3>in sort of a multiensal space, and you're trying to

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<v Speaker 3>look for similarities between vectors in this multidimensional space. So

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<v Speaker 3>you're you're basically taking text or you're taking images, and

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<v Speaker 3>you're you're vectorizing it, which is turning it into these, uh,

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<v Speaker 3>these embeddings, these numbers that can be plotted in vector space,

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<v Speaker 3>and then that allows you to find similar terms. So

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<v Speaker 3>when you're doing something like semantic search, you're trying to

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<v Speaker 3>use uh vector sort of this vector search to look

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<v Speaker 3>for similar terms. So if I if I say glass

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<v Speaker 3>and cup, I understand that those are similar terms, right,

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<v Speaker 3>because they're they're close in this vectorized space.

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<v Speaker 1>Uh.

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<v Speaker 3>Same thing with images, same thing with video. Anything that

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<v Speaker 3>can be vectorized and turned and turned into vector embeddings. Uh.

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<v Speaker 3>So we started supporting the ability to store these dense

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<v Speaker 3>vectors and query dence vectors and elastic search, and then

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<v Speaker 3>five years later generative AI happened and we were like, wow,

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<v Speaker 3>we have a vector database. That's pretty cool.

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<v Speaker 1>Yeah.

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<v Speaker 2>Right, and that kind of thing that language tookeganization just

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<v Speaker 2>lends itself so well to vectorization.

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<v Speaker 1>That's right. Well, that's what makes possible, right like chat GPT,

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<v Speaker 1>you type in a phrase or a question, the response,

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<v Speaker 1>every single word is based on the probability of what

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<v Speaker 1>is the next word based on the vector values.

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<v Speaker 3>That's right. Right, And well, so there's there's a couple

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<v Speaker 3>of ways that vectors are used. One is inside the

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<v Speaker 3>models themselves, but a vector database specifically is used for

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<v Speaker 3>helping ground an l l M. So like an LM

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<v Speaker 3>doesn't actually use a vector database, but it uses vectors

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<v Speaker 3>in how it's how it stores the information models. But

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<v Speaker 3>for a vector database, it's it's trying to get the

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<v Speaker 3>l M to ground itself on third party data so

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<v Speaker 3>that you know, if it's not been trained on a

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<v Speaker 3>product catalog, for example, you can expose it to a

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<v Speaker 3>product catalog, or if you needed to use some information

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<v Speaker 3>that it has not been specifically trained on, you can

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<v Speaker 3>ground it on that. And a vector database is one

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<v Speaker 3>technique for helping produce highly similar, highly effective search results

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<v Speaker 3>that help ground that LLLM. We actually found that the

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<v Speaker 3>best techniques are actually not just using vector databases. You know,

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<v Speaker 3>I'm a we're a vector database, and I'm gonna say

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<v Speaker 3>the best techniques are not just using the best techniques

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<v Speaker 3>are actually using combinations of technologies. Hybrid search. We're using

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<v Speaker 3>combinations of lexical search UH and uh en vector search,

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<v Speaker 3>or you're using sort of graph traversal together with vector

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<v Speaker 3>search where you're you're modeling information and we're seeing the

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<v Speaker 3>relationship between entities. But we support all these things. We

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<v Speaker 3>support a combination of geospatial search together with vector search.

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<v Speaker 3>So yeah, there's also other techniques like reranking. So once

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<v Speaker 3>you all the reason.

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<v Speaker 2>You're talk about runing multiple searches and in those different

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<v Speaker 2>forms and then somehow munging it together, so you're not

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<v Speaker 2>repeating yourself to get a results at that's.

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<v Speaker 3>Right, or you're you're running post process like you might

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<v Speaker 3>pull a bunch of results and then run reranking after

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<v Speaker 3>the fact, right, and that get further refines the results.

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<v Speaker 3>And then what you're doing is you're passing the right

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<v Speaker 3>inform to an LM so that it can say, okay,

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<v Speaker 3>you're using it. You're passing into the context window and

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<v Speaker 3>saying this is the information you should build your response on.

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<v Speaker 3>And all this happens blazing really really fast, and then

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<v Speaker 3>the LM now has some information in its context window

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<v Speaker 3>that it can use to to provide an accurate result.

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<v Speaker 1>So here's a scenario. Let's say I'm building an agent,

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<v Speaker 1>and the agent I'm giving access to my database. On

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<v Speaker 1>one side, I've got a SEQL server with a bunch

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<v Speaker 1>of relational data in it. On the other side, I

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<v Speaker 1>have a las to search vector database, and I give

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<v Speaker 1>it access to both of those things. Is it going

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<v Speaker 1>to be able to find things in the last to

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<v Speaker 1>elast to search faster than it would in my relational

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<v Speaker 1>data if they were both, if it was both trained

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<v Speaker 1>on both data.

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<v Speaker 3>We would be It depends if so I'm assuming this

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<v Speaker 3>scenario that we have a connector to that database and

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<v Speaker 3>we can run into it will be faster.

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<v Speaker 1>Okay, so yeah, so why would I want to use

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<v Speaker 1>the relational database? And my this is this is actually

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<v Speaker 1>why one of the first that's why you're here, I

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<v Speaker 1>get it. I see, actually one of the first.

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<v Speaker 2>It's the end of databasetase we know it.

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<v Speaker 3>One of the first use cases for elastic search was

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<v Speaker 3>actually database offloading because it was the search function was

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<v Speaker 3>creating contention in the database, so offloaded it to elastic

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<v Speaker 3>search and we were blazing fast. Helped speed up the

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<v Speaker 3>database at the same time.

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<v Speaker 1>You know, most people what they do is they have

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<v Speaker 1>one database for their product data or whatever, and then

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<v Speaker 1>they have another one that's optimized for searching. That's right, right,

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<v Speaker 1>So the one optimized for searching doesn't have all the

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<v Speaker 1>the joins and everything that's crazy about data.

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<v Speaker 2>Relational database cercually and we're not I don't want to

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<v Speaker 2>be mean to relational databases, but they were from a

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<v Speaker 2>time when DISPAS was at a premium, and so don't

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<v Speaker 2>striplicate data, right, Like, that's why we organize data that way.

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<v Speaker 2>It was space efficient. It's not particularly important now. Admittedly,

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<v Speaker 2>now we have an infrastructure around it where we have

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<v Speaker 2>so many querying tools and other bits and pieces that

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<v Speaker 2>just work against it. That that's your instinct, and all

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<v Speaker 2>our rms tend to point to. Okay, some ways are

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<v Speaker 2>just in place from the momentum of what's been built

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<v Speaker 2>on the facto standard, and they're highly integrated, like they

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<v Speaker 2>have their advantages, but there are more ways to store

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<v Speaker 2>data and we do. The trick is, you know, deciding

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<v Speaker 2>on a source of truth and trying to keep everything,

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<v Speaker 2>every copy in sync.

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<v Speaker 1>So a last search on Azure is kind of like

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<v Speaker 1>the place to be. I think now that we've been

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<v Speaker 1>talking about this, if you're going to be building you know,

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<v Speaker 1>copilot and agents in Azure Foundry, I guess.

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<v Speaker 3>And we've actually integrated with AI Foundry. We're one of

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<v Speaker 3>three launch partners. So if you're using AI Foundry, you

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<v Speaker 3>can use us as a grounding engine as a way

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<v Speaker 3>to build generative AI applications using our vector database. We've

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<v Speaker 3>also been integrated with semantic kernel from the very beginning. Right,

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<v Speaker 3>we first started supporting semantic kernel by offering vector search.

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<v Speaker 3>Recently we actually extended that to do hybrid search. So

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<v Speaker 3>what I was talking about before, being able to combine

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<v Speaker 3>different search techniques, right, we're the first offering to do that.

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<v Speaker 1>Now, you know, we've been given SQL server a lot

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<v Speaker 1>of crap here. But if somebody is listening and say, yeah,

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<v Speaker 1>you know that rings, I think I'm going to try this.

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<v Speaker 1>How easy is it to move our relational databases from

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<v Speaker 1>let's say SQL azure to elastic search.

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<v Speaker 3>How easy it would it be to change the search

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<v Speaker 3>to be using out using US as a search engine

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<v Speaker 3>and plugging into.

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<v Speaker 1>Or data or just moving all the data, moving all

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<v Speaker 1>the search. Is that something that you want to necessarily do?

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<v Speaker 3>Most people wouldn't use US as sort of a database

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<v Speaker 3>alternative database. They would use US if they're using a

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<v Speaker 3>relational database. They might use US as to offload search functions,

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<v Speaker 3>so they would use it that way. But people do

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<v Speaker 3>use US to store logs and other time series data

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<v Speaker 3>as a primary data store. Sure you wouldn't store that.

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<v Speaker 1>In a relational database, so not real data.

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<v Speaker 3>It's not relational data. But people do, people do. I

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<v Speaker 3>would not, I would not use this as an alternative

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<v Speaker 3>to a relational data store. But I would absolutely use

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<v Speaker 3>US as a time series database. I would absolutely use

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<v Speaker 3>US as a vector datase. I would absolutely use US

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<v Speaker 3>as a database offloading tool for SQL and other words speeding.

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<v Speaker 1>Yeah, that's right. Yeah, and so it has it has

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<v Speaker 1>access to your SEQL server. It can figure out how

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<v Speaker 1>to optimize those queries things that you would do in

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<v Speaker 1>a store procedure where you're getting multiple data sets from

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<v Speaker 1>multiple tables and things like that. That's the kind of

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<v Speaker 1>stuff where you would be in a lasts.

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<v Speaker 3>Those are the kind of workloads that are perfect for us,

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<v Speaker 3>Like we will help speed that up, take the take

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<v Speaker 3>the workload off of SQL server so that they can

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<v Speaker 3>handle the rest of its work without contention.

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<v Speaker 2>So I can also see the data, like I want

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<v Speaker 2>to search the incoming data. I think you constantly current data,

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<v Speaker 2>so I don't want to keep reprocessing models to include it.

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<v Speaker 2>So I'm using these search mechanisms that data is coming

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00:21:05.680 --> 00:21:08.240
<v Speaker 2>into my relational database, then probably I have an asynchronous

424
00:21:08.319 --> 00:21:11.359
<v Speaker 2>call from that calling out of it. And reorganizing it

425
00:21:11.400 --> 00:21:12.480
<v Speaker 2>into the vector database.

426
00:21:13.680 --> 00:21:16.359
<v Speaker 3>Yeah, so there would be the connector would would basically

427
00:21:16.920 --> 00:21:19.960
<v Speaker 3>be indexing a as is coming in, say, creating an

428
00:21:19.960 --> 00:21:22.759
<v Speaker 3>index so that it can handle those queries and do

429
00:21:22.839 --> 00:21:26.039
<v Speaker 3>that without bothering the primary database.

430
00:21:26.119 --> 00:21:28.359
<v Speaker 2>Right, yeah, yeah, so is that actually a hook I

431
00:21:28.400 --> 00:21:31.640
<v Speaker 2>put into a lastic search into elastica just saying hey,

432
00:21:31.640 --> 00:21:34.599
<v Speaker 2>watch this database when yous come in politic exactly now,

433
00:21:34.960 --> 00:21:36.839
<v Speaker 2>I mean, because it's relation to database, I tend to

434
00:21:36.880 --> 00:21:40.359
<v Speaker 2>decompose data, right, I've broken this order down into rows

435
00:21:40.400 --> 00:21:43.079
<v Speaker 2>and columns and stuff. I don't imagine that's how you

436
00:21:43.079 --> 00:21:44.799
<v Speaker 2>want to start in a vector database. You kind of

437
00:21:44.880 --> 00:21:45.119
<v Speaker 2>in a.

438
00:21:45.160 --> 00:21:50.000
<v Speaker 3>Vectora less to search, everything is sort of document based.

439
00:21:50.359 --> 00:21:55.079
<v Speaker 3>Everything's turned into a document. Uh but uh but yeah,

440
00:21:55.079 --> 00:21:58.000
<v Speaker 3>we'd be able to map that back to the rows

441
00:21:58.039 --> 00:21:59.240
<v Speaker 3>and tables in a in.

442
00:21:59.200 --> 00:22:02.720
<v Speaker 2>A in a Right. So I mean I started off

443
00:22:02.759 --> 00:22:04.759
<v Speaker 2>with the whole order. Should I just stole the whole

444
00:22:04.880 --> 00:22:08.440
<v Speaker 2>order in in elastic and then decompose it into rows

445
00:22:08.440 --> 00:22:10.400
<v Speaker 2>and columns? Like, what's the right flow there?

446
00:22:10.759 --> 00:22:14.319
<v Speaker 1>Right? If you're do you have any actual to help me?

447
00:22:17.079 --> 00:22:19.960
<v Speaker 3>I'm not sure I think you you know, if you

448
00:22:20.119 --> 00:22:22.519
<v Speaker 3>just use our connectors it'll handle this for you and

449
00:22:22.519 --> 00:22:25.559
<v Speaker 3>you'll have to think about it. It doesn't like we

450
00:22:25.599 --> 00:22:29.839
<v Speaker 3>have four hundred different connectors that have been built up

451
00:22:29.880 --> 00:22:32.000
<v Speaker 3>over the years for all kinds of different data stores

452
00:22:32.079 --> 00:22:36.559
<v Speaker 3>for everything from Snowflake to SQL server to you know

453
00:22:36.759 --> 00:22:39.559
<v Speaker 3>other and it handles all of that for sure. And

454
00:22:39.599 --> 00:22:42.119
<v Speaker 3>then these days everything is kind of getting replaced by

455
00:22:42.160 --> 00:22:45.680
<v Speaker 3>mcps or we're starting to build mcps of board into

456
00:22:45.680 --> 00:22:46.240
<v Speaker 3>that as well.

457
00:22:46.599 --> 00:22:49.160
<v Speaker 2>Yeah, the AI equation of this is flipping huge.

458
00:22:48.920 --> 00:22:51.480
<v Speaker 1>And we'll talk about that a little bit more when

459
00:22:51.480 --> 00:22:53.519
<v Speaker 1>we come back, and also next week with Scott Hunter.

460
00:22:54.279 --> 00:22:55.920
<v Speaker 1>We're going to take a break right now, though we'll

461
00:22:55.960 --> 00:22:58.880
<v Speaker 1>be back after these very important messages. Don't go away.

462
00:23:01.240 --> 00:23:03.960
<v Speaker 1>You know, dot net six has officially reached the end

463
00:23:04.000 --> 00:23:07.200
<v Speaker 1>of support and now is the time to upgrade. Dot

464
00:23:07.279 --> 00:23:10.599
<v Speaker 1>Net eight is well supported on a w S. Learn

465
00:23:10.640 --> 00:23:14.119
<v Speaker 1>more at aws dot Amazon dot com, slash dot net.

466
00:23:18.000 --> 00:23:20.359
<v Speaker 1>And we're back. It's dot net Rocks. I'm Carl Franklin

467
00:23:21.079 --> 00:23:25.599
<v Speaker 1>Campbell and we're talking to Ken Exner from Elastic about

468
00:23:25.599 --> 00:23:29.599
<v Speaker 1>elasti search and some really important information that we just

469
00:23:29.880 --> 00:23:33.319
<v Speaker 1>discovered about you know, what it, what it is, where

470
00:23:33.359 --> 00:23:37.000
<v Speaker 1>it fits in, and what it doesn't do. Uh, and

471
00:23:37.039 --> 00:23:40.759
<v Speaker 1>how it fits in with with Azure, especially about how

472
00:23:40.799 --> 00:23:46.599
<v Speaker 1>you can make searches stinky fast rights stinky fast?

473
00:23:46.680 --> 00:23:49.039
<v Speaker 3>Is that I'm not That's good.

474
00:23:49.119 --> 00:23:50.000
<v Speaker 1>No, that's really good.

475
00:23:50.480 --> 00:23:52.480
<v Speaker 2>So fast you can smell the burn and rubber.

476
00:23:53.039 --> 00:23:56.559
<v Speaker 3>That makes it better. I like that, all right?

477
00:23:56.920 --> 00:24:00.960
<v Speaker 1>So how do you get started with jam ai with

478
00:24:01.079 --> 00:24:04.920
<v Speaker 1>elastic and Azure and the Elastic AI ecosystem through Azure

479
00:24:05.200 --> 00:24:06.559
<v Speaker 1>Foundry AI Foundry.

480
00:24:06.599 --> 00:24:10.799
<v Speaker 3>Well, I guess if you're if you're uh using AI

481
00:24:10.920 --> 00:24:14.519
<v Speaker 3>foundry or using semantic Kernel, we're there, Like if you're

482
00:24:14.599 --> 00:24:16.759
<v Speaker 3>if you're choosing to use that, like if you're using

483
00:24:16.759 --> 00:24:19.759
<v Speaker 3>semantic kernel as a way to build uh generative A

484
00:24:19.920 --> 00:24:23.319
<v Speaker 3>applications into whatever you're doing. Uh, we are integrated there

485
00:24:23.359 --> 00:24:25.759
<v Speaker 3>so you can use us as a vector of database

486
00:24:25.799 --> 00:24:26.240
<v Speaker 3>within that.

487
00:24:27.240 --> 00:24:28.920
<v Speaker 2>It's just a feature. It's just a feature to.

488
00:24:28.839 --> 00:24:30.759
<v Speaker 3>Turn on, so you can go and like, can you

489
00:24:30.799 --> 00:24:32.799
<v Speaker 3>pick which vector database you want? There's a drop down,

490
00:24:32.799 --> 00:24:34.599
<v Speaker 3>there's a couple options there. We're one of those two.

491
00:24:34.799 --> 00:24:35.319
<v Speaker 1>It's great.

492
00:24:35.440 --> 00:24:37.519
<v Speaker 3>Uh. And then the same thing for AI Foundry. We're

493
00:24:37.599 --> 00:24:41.000
<v Speaker 3>integrated into AI Foundry into your workflow so you don't

494
00:24:41.039 --> 00:24:44.440
<v Speaker 3>have to think about it, so you can basically, uh,

495
00:24:44.640 --> 00:24:46.880
<v Speaker 3>you know, start using AI foundry select us as a

496
00:24:46.920 --> 00:24:47.759
<v Speaker 3>vector database.

497
00:24:48.279 --> 00:24:48.519
<v Speaker 1>Uh.

498
00:24:48.559 --> 00:24:51.720
<v Speaker 3>With our integration into Azure, it's automatically set up for you,

499
00:24:51.799 --> 00:24:54.319
<v Speaker 3>so you don't have to think about any permissions anything.

500
00:24:54.359 --> 00:24:57.599
<v Speaker 3>It's all integrated with the Azure as your client.

501
00:24:57.559 --> 00:25:00.000
<v Speaker 1>I hate to derail you, but this is the first

502
00:25:00.079 --> 00:25:03.119
<v Speaker 1>time AI Foundry has been used on dot net rocks.

503
00:25:03.359 --> 00:25:06.240
<v Speaker 1>I just learned about it today. Ah, so people probably

504
00:25:06.319 --> 00:25:07.599
<v Speaker 1>won't know what this is.

505
00:25:07.880 --> 00:25:10.599
<v Speaker 3>It's you can think of it as the new studio

506
00:25:10.799 --> 00:25:12.000
<v Speaker 3>for all things AI.

507
00:25:12.160 --> 00:25:12.759
<v Speaker 2>It's like the.

508
00:25:12.680 --> 00:25:15.599
<v Speaker 1>Azure AI portal section for AI.

509
00:25:15.799 --> 00:25:19.559
<v Speaker 3>Right, Yeah, it's yeah. There there were lots of lots

510
00:25:19.559 --> 00:25:23.279
<v Speaker 3>of studios, lots of different tools for building AI applications

511
00:25:23.319 --> 00:25:29.720
<v Speaker 3>within the Microsoft ecosystem, AI Studio, Open AI Studio, a

512
00:25:29.759 --> 00:25:31.880
<v Speaker 3>bunch of them that kind of got co pilots do

513
00:25:32.000 --> 00:25:36.839
<v Speaker 3>Copilot Studio, that's right. So, uh, the UH AI Foundry

514
00:25:36.920 --> 00:25:39.279
<v Speaker 3>is sort of the the new integrated version of that.

515
00:25:39.680 --> 00:25:42.480
<v Speaker 3>It gives dot net developers one place to go to

516
00:25:42.920 --> 00:25:45.039
<v Speaker 3>if they're building these AI applications.

517
00:25:45.079 --> 00:25:47.079
<v Speaker 1>All right, so that's where you go on Azure if

518
00:25:47.119 --> 00:25:49.279
<v Speaker 1>you want to get started, I guess you know, if

519
00:25:49.319 --> 00:25:53.160
<v Speaker 1>you want to start building agents and and all of that.

520
00:25:53.519 --> 00:25:56.079
<v Speaker 2>So when you toggle the integration, is it how is

521
00:25:56.119 --> 00:25:58.680
<v Speaker 2>it deploying elastic that point or are you using the

522
00:25:58.680 --> 00:26:01.119
<v Speaker 2>server list version? Like, what's what's the actual impact?

523
00:26:01.519 --> 00:26:04.119
<v Speaker 3>You can if you've set it up already, you're just

524
00:26:04.440 --> 00:26:08.200
<v Speaker 3>picking your pointing to a pointing to an existing cluster

525
00:26:08.400 --> 00:26:09.640
<v Speaker 3>or an existing project.

526
00:26:09.720 --> 00:26:13.200
<v Speaker 2>In the serverless version, that might be your VM, or.

527
00:26:13.279 --> 00:26:14.880
<v Speaker 3>If you're wanting to create it from scratch, you can

528
00:26:14.920 --> 00:26:17.960
<v Speaker 3>do that as well. Okay, so it's a seamless integration,

529
00:26:18.000 --> 00:26:21.359
<v Speaker 3>which which is pretty amazing for you know, being a

530
00:26:21.400 --> 00:26:24.799
<v Speaker 3>third party product. We're always pushing for these tight integrations

531
00:26:25.319 --> 00:26:26.519
<v Speaker 3>and we've gotten that with Azure.

532
00:26:26.599 --> 00:26:28.799
<v Speaker 2>Now people like a checkbox and things just happen. It's

533
00:26:28.839 --> 00:26:32.039
<v Speaker 2>just I'm putting my administrator's hat on. It's like the

534
00:26:32.079 --> 00:26:34.640
<v Speaker 2>dev check that box and a VM appeared. In my world,

535
00:26:36.359 --> 00:26:38.240
<v Speaker 2>I'd be more emotional about that. Where if it's the

536
00:26:38.279 --> 00:26:40.599
<v Speaker 2>serverles offering, where it's just now we're just going to

537
00:26:40.640 --> 00:26:42.839
<v Speaker 2>get a new bill. That's the cfo's problem. I don't

538
00:26:42.839 --> 00:26:43.480
<v Speaker 2>mind that at all.

539
00:26:44.440 --> 00:26:44.920
<v Speaker 1>That's right.

540
00:26:46.079 --> 00:26:47.920
<v Speaker 2>So is it like do you get to pick the

541
00:26:48.440 --> 00:26:50.680
<v Speaker 2>deploy mode or is it automatically.

542
00:26:50.519 --> 00:26:54.160
<v Speaker 3>The server This one is not enabled yet. Yeah, the servers,

543
00:26:54.160 --> 00:26:54.400
<v Speaker 3>I mean.

544
00:26:54.359 --> 00:26:56.480
<v Speaker 2>We're talking the day you announced it, so that's fair.

545
00:26:57.240 --> 00:27:00.799
<v Speaker 3>But the the hosted version of our cloud offering is there,

546
00:27:00.880 --> 00:27:01.200
<v Speaker 3>you can.

547
00:27:01.200 --> 00:27:02.920
<v Speaker 2>You can r so you could be hosted by you.

548
00:27:04.640 --> 00:27:05.400
<v Speaker 1>Yeah, we'll be.

549
00:27:05.400 --> 00:27:08.279
<v Speaker 3>Going ga with our server list offering in the coming

550
00:27:08.319 --> 00:27:10.880
<v Speaker 3>weeks and then at that point you'd be able to

551
00:27:10.880 --> 00:27:12.319
<v Speaker 3>just select that project as well.

552
00:27:12.839 --> 00:27:15.119
<v Speaker 2>And is that running in your infrastructure? Is it an Azure?

553
00:27:15.279 --> 00:27:16.200
<v Speaker 1>It's on Azure? Okay?

554
00:27:16.240 --> 00:27:17.640
<v Speaker 3>Yeah, yeah, it's all on Azure.

555
00:27:17.720 --> 00:27:19.880
<v Speaker 1>So can you give us an idea of how much

556
00:27:19.920 --> 00:27:25.079
<v Speaker 1>it costs, I mean relative to other Azure resources?

557
00:27:25.119 --> 00:27:28.119
<v Speaker 3>Maybe, well if you're so. There's three different project types.

558
00:27:28.880 --> 00:27:32.640
<v Speaker 3>There's the traditional search one, which you would handle use

559
00:27:32.720 --> 00:27:37.759
<v Speaker 3>for typical search or vector search workloads. There's the observability one,

560
00:27:37.920 --> 00:27:41.839
<v Speaker 3>which is sort of built around observability capabilities like log

561
00:27:41.880 --> 00:27:45.799
<v Speaker 3>analytics and metrics. And then there's a security one because

562
00:27:45.839 --> 00:27:47.440
<v Speaker 3>one of the things that happened along the way is

563
00:27:47.759 --> 00:27:51.519
<v Speaker 3>people thread hunters started to use us as a security

564
00:27:51.559 --> 00:27:56.599
<v Speaker 3>analytics platform, so we created entire product packaging of Elastic

565
00:27:57.200 --> 00:28:00.480
<v Speaker 3>for security workloads. So for threat hunters, Wow, if you're

566
00:28:00.559 --> 00:28:06.599
<v Speaker 3>using the observability or the security project, type. It's basically

567
00:28:07.000 --> 00:28:08.559
<v Speaker 3>we just charge for the data going in and the

568
00:28:08.640 --> 00:28:11.119
<v Speaker 3>data retained. If you're not using any data, you don't

569
00:28:11.119 --> 00:28:14.240
<v Speaker 3>get charged to anything as simple as that. For the

570
00:28:14.319 --> 00:28:17.759
<v Speaker 3>search one we charge. We call it a VCU. It's

571
00:28:17.759 --> 00:28:21.880
<v Speaker 3>a virtual compute unit, and it scales to near zero.

572
00:28:22.079 --> 00:28:24.400
<v Speaker 3>There's always a little bit of charge because we have

573
00:28:24.440 --> 00:28:26.559
<v Speaker 3>to keep some cashes warm to handle any kind of

574
00:28:26.559 --> 00:28:28.920
<v Speaker 3>incoming query. But it's like less than twenty bucks a

575
00:28:28.920 --> 00:28:33.279
<v Speaker 3>month at the start, so pretty, you know, scales down

576
00:28:33.319 --> 00:28:33.960
<v Speaker 3>to near zero.

577
00:28:34.039 --> 00:28:36.119
<v Speaker 1>That's really cool. Yeah, good to know. All right, So

578
00:28:37.160 --> 00:28:41.720
<v Speaker 1>just a selfish question. Maybe the other listeners are thinking

579
00:28:41.759 --> 00:28:44.519
<v Speaker 1>this too. All right, I'm committed to using Elasti search.

580
00:28:44.640 --> 00:28:46.880
<v Speaker 1>I've got a relational database that has on my Goo

581
00:28:46.920 --> 00:28:48.880
<v Speaker 1>in it. I got a lot of store procedures, and

582
00:28:48.880 --> 00:28:51.400
<v Speaker 1>I got some views and stuff. How do I take

583
00:28:51.559 --> 00:28:55.880
<v Speaker 1>all that t SQL in a big store procedure that

584
00:28:55.920 --> 00:28:59.319
<v Speaker 1>returns multiple record sets from multiple places and turn that

585
00:28:59.400 --> 00:29:03.319
<v Speaker 1>into a lab to search. Is there a language I do?

586
00:29:03.519 --> 00:29:08.440
<v Speaker 3>Is there a good migration into it? Yeah? Well, I'm

587
00:29:08.519 --> 00:29:10.599
<v Speaker 3>I'm hoping we can build something with l MS that

588
00:29:10.599 --> 00:29:11.000
<v Speaker 3>could do that.

589
00:29:11.079 --> 00:29:11.279
<v Speaker 1>Migra.

590
00:29:11.519 --> 00:29:13.359
<v Speaker 2>Yeah I do too, actually.

591
00:29:13.119 --> 00:29:14.319
<v Speaker 1>Because I don't want to do it.

592
00:29:13.960 --> 00:29:18.559
<v Speaker 3>It's so the there is. So we actually have our

593
00:29:18.599 --> 00:29:21.880
<v Speaker 3>own uh sort of query language. It's called e s

594
00:29:21.960 --> 00:29:23.880
<v Speaker 3>q O and it's a piped query language. So we

595
00:29:23.960 --> 00:29:28.640
<v Speaker 3>use pipes and you can very power, very very linux

596
00:29:28.720 --> 00:29:31.559
<v Speaker 3>very powers. Yeah, so you use pipes to pass uh,

597
00:29:31.680 --> 00:29:34.519
<v Speaker 3>since you one query into another and it's a way

598
00:29:34.519 --> 00:29:38.680
<v Speaker 3>of combining and uh, it's a very powerful query language.

599
00:29:39.000 --> 00:29:42.079
<v Speaker 3>We introduced joins recently. Uh, so it can do a

600
00:29:42.119 --> 00:29:44.079
<v Speaker 3>lot of the things that you can with with the

601
00:29:44.119 --> 00:29:47.920
<v Speaker 3>traditional sequel language, but that's becoming our default language to

602
00:29:48.000 --> 00:29:51.920
<v Speaker 3>that thing, and at least for analytical workloads. So if youre

603
00:29:51.960 --> 00:29:53.799
<v Speaker 3>using this for search, you would just use the search API,

604
00:29:53.920 --> 00:29:57.960
<v Speaker 3>but for analytical workloads, uh, you'd use this new query language.

605
00:29:58.119 --> 00:30:01.759
<v Speaker 1>So these conversions seem like a perfect job for an AI.

606
00:30:02.000 --> 00:30:04.440
<v Speaker 3>H oh yeah, yeah, so like this is this is

607
00:30:04.440 --> 00:30:08.519
<v Speaker 3>actually something we do right now. People might use other

608
00:30:09.160 --> 00:30:12.359
<v Speaker 3>solutions like Spunk, they have their own query language. People

609
00:30:12.480 --> 00:30:14.720
<v Speaker 3>have used l o ms to the conversion of one

610
00:30:14.799 --> 00:30:19.400
<v Speaker 3>query language into another. They're surprisingly accurate. So yeah, there's

611
00:30:19.440 --> 00:30:22.599
<v Speaker 3>no reason. You know, once we get to sort of

612
00:30:22.640 --> 00:30:26.599
<v Speaker 3>the functionality parody that you need for for SQL, that

613
00:30:26.680 --> 00:30:27.440
<v Speaker 3>you couldn't.

614
00:30:27.079 --> 00:30:28.359
<v Speaker 1>Just use an l O And when'd you say your

615
00:30:28.440 --> 00:30:29.240
<v Speaker 1>language is called.

616
00:30:29.079 --> 00:30:31.960
<v Speaker 3>Again e SQL query language.

617
00:30:32.000 --> 00:30:33.960
<v Speaker 1>Yeah, all right, so there's a good time kids to

618
00:30:34.000 --> 00:30:36.599
<v Speaker 1>go out and learn the SQL, at least a little

619
00:30:36.599 --> 00:30:39.519
<v Speaker 1>bit to know what you're going to be doing next

620
00:30:39.559 --> 00:30:43.119
<v Speaker 1>year or maybe in a few months, who knows, or

621
00:30:43.920 --> 00:30:47.000
<v Speaker 1>this afternoon or this afternoon really ambitious.

622
00:30:47.000 --> 00:30:50.680
<v Speaker 2>Generally, my experience when you're moving to non relational databases

623
00:30:50.839 --> 00:30:54.799
<v Speaker 2>is you're calling APIs. It's basically just a coding exercise.

624
00:30:54.839 --> 00:30:57.359
<v Speaker 2>You're different kind of data fetch and start, start effects

625
00:30:57.359 --> 00:30:59.640
<v Speaker 2>and things like that. It's a different way thinking about it.

626
00:30:59.640 --> 00:31:02.799
<v Speaker 2>And part of that to me is you're now handling

627
00:31:02.839 --> 00:31:05.839
<v Speaker 2>the data differently like the query. The old query doesn't

628
00:31:05.920 --> 00:31:07.759
<v Speaker 2>make sense if the data is stored in a different way.

629
00:31:07.759 --> 00:31:10.599
<v Speaker 2>There there are no joins or not joins the way

630
00:31:10.640 --> 00:31:11.440
<v Speaker 2>you're thinking of them.

631
00:31:11.440 --> 00:31:13.839
<v Speaker 3>No, it's not a join like it is a relation

632
00:31:14.519 --> 00:31:18.119
<v Speaker 3>creating a join on sort of a no sequel data

633
00:31:18.119 --> 00:31:20.640
<v Speaker 3>sert where we use it's a document based data system.

634
00:31:20.960 --> 00:31:22.359
<v Speaker 3>Was challenging. How do you do that?

635
00:31:22.519 --> 00:31:22.720
<v Speaker 2>Yeah?

636
00:31:23.920 --> 00:31:26.119
<v Speaker 3>Uh yeah, it's a different way of interacting with data.

637
00:31:26.480 --> 00:31:27.880
<v Speaker 3>It's a document based orientation.

638
00:31:28.039 --> 00:31:30.920
<v Speaker 2>Yeah, so you know the document the experience you've had

639
00:31:30.960 --> 00:31:33.160
<v Speaker 2>to working with document databases is the right sort of

640
00:31:33.200 --> 00:31:38.039
<v Speaker 2>approach to organizing things like I would be hesitant to

641
00:31:38.240 --> 00:31:41.359
<v Speaker 2>just move a relational database to a vector database or

642
00:31:41.480 --> 00:31:44.400
<v Speaker 2>area a document store of any kind just because the

643
00:31:44.599 --> 00:31:45.720
<v Speaker 2>data is organized differently.

644
00:31:45.880 --> 00:31:50.240
<v Speaker 3>Which organized differently you're going to augment it with ectory base.

645
00:31:50.359 --> 00:31:51.720
<v Speaker 2>But now I like this idea of yeah, my order

646
00:31:51.759 --> 00:31:53.759
<v Speaker 2>is still decomposed in the relational database. We have all

647
00:31:53.759 --> 00:31:55.039
<v Speaker 2>the reporting and so f on that, but I also

648
00:31:55.119 --> 00:31:57.160
<v Speaker 2>keep a copy and in the document start that's the

649
00:31:57.279 --> 00:32:00.400
<v Speaker 2>entire order, and still got all those elements senate that

650
00:32:00.440 --> 00:32:02.799
<v Speaker 2>are searchable and findable and so forth. That's right, but

651
00:32:03.200 --> 00:32:04.880
<v Speaker 2>when you find any element of it, you find the

652
00:32:04.880 --> 00:32:05.240
<v Speaker 2>whole thing.

653
00:32:05.359 --> 00:32:07.319
<v Speaker 1>So would you just call Kim Trip and Paul Randall

654
00:32:07.319 --> 00:32:08.799
<v Speaker 1>and say, hey, optimize my indexes?

655
00:32:08.960 --> 00:32:09.359
<v Speaker 3>Pretty sure?

656
00:32:09.480 --> 00:32:12.960
<v Speaker 1>No, No, get kind of retired. Actually yeah, there's that,

657
00:32:12.599 --> 00:32:15.839
<v Speaker 1>but I mean optimize the indexes. I mean, that's that's

658
00:32:16.119 --> 00:32:19.039
<v Speaker 1>kind of where people go when they have search problem

659
00:32:19.319 --> 00:32:23.519
<v Speaker 1>performance on SQL server. Yeah, well, in the.

660
00:32:23.559 --> 00:32:26.440
<v Speaker 3>Lastic search, that's completely everything is indexed. You don't you

661
00:32:26.480 --> 00:32:28.599
<v Speaker 3>don't you don't pick you know what you want to index.

662
00:32:28.640 --> 00:32:30.440
<v Speaker 3>Everything is index so that's not the.

663
00:32:30.480 --> 00:32:32.559
<v Speaker 1>End, and the next you're created on the fly?

664
00:32:32.640 --> 00:32:35.359
<v Speaker 3>Are they created as you're ingesting? Yeah, so it's all

665
00:32:35.440 --> 00:32:36.640
<v Speaker 3>automatically created for you.

666
00:32:37.480 --> 00:32:39.880
<v Speaker 1>Any good document database, that's right. Yeah.

667
00:32:39.640 --> 00:32:41.680
<v Speaker 2>So, and I find it interesting to have the sort

668
00:32:41.720 --> 00:32:44.599
<v Speaker 2>of three species there, like this security makes sense, that's

669
00:32:44.599 --> 00:32:47.960
<v Speaker 2>a special thing. But the observability and this is the

670
00:32:48.119 --> 00:32:50.119
<v Speaker 2>This is why I pulled that comment from the listener,

671
00:32:50.319 --> 00:32:53.519
<v Speaker 2>was that guy was doing observability before it was a product.

672
00:32:53.599 --> 00:32:54.359
<v Speaker 1>Yeah.

673
00:32:54.480 --> 00:32:56.880
<v Speaker 3>So the interesting thing about search, One of the things

674
00:32:56.920 --> 00:33:00.160
<v Speaker 3>that we've learned over the years is that search which

675
00:33:00.160 --> 00:33:02.599
<v Speaker 3>is kind of everywhere, Like people start using search to

676
00:33:02.640 --> 00:33:07.440
<v Speaker 3>build applications. It's not just about Initially when plastic search

677
00:33:08.359 --> 00:33:10.359
<v Speaker 3>came out, it was about adding search to a website

678
00:33:10.440 --> 00:33:13.079
<v Speaker 3>or adding it to a product search. That's right, So

679
00:33:13.279 --> 00:33:15.839
<v Speaker 3>you know, you see what e commerce sites starting to

680
00:33:15.839 --> 00:33:20.039
<v Speaker 3>integrate US. But people started building applications on top of US.

681
00:33:20.079 --> 00:33:24.759
<v Speaker 3>They started building like matchmaking sites, or signal intelligence systems

682
00:33:24.799 --> 00:33:27.720
<v Speaker 3>or fraud detection systems. It became sort of a platform

683
00:33:27.759 --> 00:33:30.480
<v Speaker 3>for building things. But one of the most popular was

684
00:33:30.519 --> 00:33:33.680
<v Speaker 3>people using US as a log analytics solution, right, using

685
00:33:33.759 --> 00:33:37.200
<v Speaker 3>US to store and query logs. So rather than you know,

686
00:33:37.279 --> 00:33:39.720
<v Speaker 3>do and doing grap They would use us as a solution,

687
00:33:39.920 --> 00:33:43.559
<v Speaker 3>ship blogs to us and then run run fast queries

688
00:33:43.559 --> 00:33:46.359
<v Speaker 3>on it. From there, people said, hey, if I'm going

689
00:33:46.400 --> 00:33:48.640
<v Speaker 3>to use you for logs, can I use you for metrics?

690
00:33:48.640 --> 00:33:50.200
<v Speaker 3>Can I use you for tracing? Can I use you

691
00:33:50.240 --> 00:33:54.960
<v Speaker 3>for other things? Right, we started providing a complete observability platform.

692
00:33:54.799 --> 00:33:56.440
<v Speaker 2>And then that's the whole thing. Is like part of

693
00:33:56.440 --> 00:33:59.599
<v Speaker 2>the challenge of doing this is all the different sources.

694
00:33:59.599 --> 00:34:01.240
<v Speaker 2>So the fact that you have a place to drop

695
00:34:01.279 --> 00:34:03.799
<v Speaker 2>all those sources routinely, so it's already in one spot

696
00:34:03.920 --> 00:34:04.599
<v Speaker 2>and it's.

697
00:34:04.519 --> 00:34:05.920
<v Speaker 3>All in one one data story.

698
00:34:06.000 --> 00:34:08.599
<v Speaker 2>Yeah, and a quick one that can do a joint

699
00:34:08.639 --> 00:34:10.639
<v Speaker 2>between those different rest laces when you need to.

700
00:34:10.800 --> 00:34:12.679
<v Speaker 3>Well, this is actually one of the one of the

701
00:34:12.719 --> 00:34:15.079
<v Speaker 3>things I love to show about ESQL is being able

702
00:34:15.119 --> 00:34:19.599
<v Speaker 3>to do a query across metrics and logs and traces

703
00:34:19.639 --> 00:34:22.079
<v Speaker 3>and kind of blows people's minds, like, I'm going to

704
00:34:22.639 --> 00:34:26.320
<v Speaker 3>do a query that joins data and creates this, show

705
00:34:26.400 --> 00:34:30.440
<v Speaker 3>me all the all the hosts that have you know,

706
00:34:30.519 --> 00:34:33.639
<v Speaker 3>ninety percent CPU and that have had a log in

707
00:34:33.679 --> 00:34:36.840
<v Speaker 3>failure over the last hour, and that have had and

708
00:34:36.920 --> 00:34:40.079
<v Speaker 3>you can combine different types of queries across different signal types,

709
00:34:40.119 --> 00:34:41.719
<v Speaker 3>and it's kind of magical.

710
00:34:41.360 --> 00:34:42.400
<v Speaker 2>And it's powerful.

711
00:34:42.480 --> 00:34:45.960
<v Speaker 1>So I know that our listeners probably know more about

712
00:34:46.000 --> 00:34:50.440
<v Speaker 1>Azure than they do about Elastic, and your audience probably

713
00:34:50.480 --> 00:34:54.000
<v Speaker 1>knows more about Elastic than about Azure. Is there? Is

714
00:34:54.039 --> 00:34:55.159
<v Speaker 1>that a fair statement?

715
00:34:55.199 --> 00:34:55.960
<v Speaker 3>I think that's fair.

716
00:34:56.079 --> 00:34:59.440
<v Speaker 1>Yeah, that's fair. Yeah, And so what can we tell

717
00:34:59.480 --> 00:35:02.039
<v Speaker 1>these people about Azure if there if this is a

718
00:35:02.159 --> 00:35:06.159
<v Speaker 1>new concept, do you know? I guess I have a

719
00:35:06.159 --> 00:35:09.480
<v Speaker 1>customer right now that's all on prem and was picking

720
00:35:09.480 --> 00:35:12.440
<v Speaker 1>my brain about moving to Azure, you know, and my

721
00:35:12.800 --> 00:35:15.599
<v Speaker 1>response was, well, why aren't you there? Now?

722
00:35:15.719 --> 00:35:15.920
<v Speaker 3>You know?

723
00:35:16.000 --> 00:35:20.800
<v Speaker 1>It's like, because there's an inherent fear. This is my

724
00:35:21.079 --> 00:35:24.800
<v Speaker 1>this is my word, it's the word of the week. Yeah,

725
00:35:24.800 --> 00:35:27.199
<v Speaker 1>there's a fear by people that I think they don't

726
00:35:27.239 --> 00:35:30.519
<v Speaker 1>trust moving their data somewhere else where they can't keep

727
00:35:30.519 --> 00:35:33.440
<v Speaker 1>a watch on it. And my my response is, all right,

728
00:35:33.559 --> 00:35:36.880
<v Speaker 1>so you have how many guys in your I T department? Two? Three? Okay?

729
00:35:37.039 --> 00:35:39.599
<v Speaker 1>Do they all sleep at the same time? Yeah? Okay,

730
00:35:39.639 --> 00:35:42.840
<v Speaker 1>So you know, in Azure, you've got hundreds probably of

731
00:35:42.960 --> 00:35:46.679
<v Speaker 1>people all around the world that are watching over your

732
00:35:46.760 --> 00:35:50.119
<v Speaker 1>data while you sleep, while you're awake, and who do

733
00:35:50.119 --> 00:35:54.599
<v Speaker 1>you trust more Joe in It, who's a sleep eight

734
00:35:54.639 --> 00:35:57.440
<v Speaker 1>hours of the day, or you know, the the Azure

735
00:35:57.480 --> 00:36:00.960
<v Speaker 1>team that is watching over your fate at twenty four

736
00:36:01.000 --> 00:36:03.440
<v Speaker 1>to seven. So that's my pitch. What do you what

737
00:36:03.480 --> 00:36:06.920
<v Speaker 1>would you say to your your customers.

738
00:36:06.440 --> 00:36:10.239
<v Speaker 3>About why they should move to Azure or yeah, yeah,

739
00:36:10.320 --> 00:36:13.800
<v Speaker 3>if you're on prem. Uh, you know, every every company

740
00:36:13.840 --> 00:36:16.159
<v Speaker 3>that's started on prem is starting to think about the cloud.

741
00:36:16.159 --> 00:36:20.119
<v Speaker 3>They've had this conversation for ten fifteen years. At this point,

742
00:36:20.159 --> 00:36:21.519
<v Speaker 3>you're kind of feeling.

743
00:36:21.239 --> 00:36:24.960
<v Speaker 1>Like a late to the game. Yeah, but they're still

744
00:36:25.000 --> 00:36:28.639
<v Speaker 1>out there obviously, Yeah, just having a conversation. Yeah.

745
00:36:28.679 --> 00:36:32.039
<v Speaker 3>So but yeah, I think most customers should be thinking

746
00:36:32.039 --> 00:36:36.039
<v Speaker 3>about it. If they aren't already, you're not going to

747
00:36:36.039 --> 00:36:40.320
<v Speaker 3>get the efficiencies of doing it yourself. There was there

748
00:36:40.400 --> 00:36:42.679
<v Speaker 3>was this movement a few years ago where people started

749
00:36:42.719 --> 00:36:46.719
<v Speaker 3>going back to their own data centers because they thought

750
00:36:46.719 --> 00:36:49.480
<v Speaker 3>they could do it cheaper. And you can't, Like, you can't.

751
00:36:49.840 --> 00:36:53.400
<v Speaker 3>You don't get the efficiencies at the scale that the

752
00:36:53.679 --> 00:36:56.360
<v Speaker 3>cloud providers get like Azure. You don't get the like

753
00:36:56.400 --> 00:36:59.480
<v Speaker 3>the ability to buy bandwidth in huge amounts. If you're

754
00:36:59.480 --> 00:37:01.679
<v Speaker 3>trying to buy bandwidth by yourself. You're trying to buy

755
00:37:01.719 --> 00:37:03.920
<v Speaker 3>servers by yourself, you're gonna pay a lot more. Sure,

756
00:37:04.400 --> 00:37:07.000
<v Speaker 3>so any any savings that you think you can get

757
00:37:07.400 --> 00:37:08.880
<v Speaker 3>is just going to be wiped away by.

758
00:37:08.840 --> 00:37:11.239
<v Speaker 1>Especially if you're paying by the transaction. That's right.

759
00:37:11.639 --> 00:37:14.159
<v Speaker 2>Yeah, but yeah, we did this whole series on run

760
00:37:14.199 --> 00:37:16.400
<v Speaker 2>Ass Radio where a whole bunch of folks list lifted

761
00:37:16.400 --> 00:37:18.239
<v Speaker 2>and shifted their vms into the cloud. Then we're shocked

762
00:37:18.239 --> 00:37:20.679
<v Speaker 2>when the bill was big, Yeah, not recognizing you've run

763
00:37:20.719 --> 00:37:22.559
<v Speaker 2>those vms twenty four hours a day, like you're paying

764
00:37:22.559 --> 00:37:24.679
<v Speaker 2>twenty four hours a day for them. Yeah, and then

765
00:37:24.800 --> 00:37:27.760
<v Speaker 2>moving up to the platform pieces was more efficient, but

766
00:37:28.039 --> 00:37:30.639
<v Speaker 2>then they haul them back and they're just ignoring a

767
00:37:30.679 --> 00:37:32.800
<v Speaker 2>bunch of the numbers. You know, you pay for the

768
00:37:32.800 --> 00:37:35.639
<v Speaker 2>hardware once, so you forget about advertising across the period

769
00:37:36.119 --> 00:37:38.639
<v Speaker 2>much less the replacement costs, and you know, all those

770
00:37:38.679 --> 00:37:39.840
<v Speaker 2>other pieces to go into it.

771
00:37:39.800 --> 00:37:42.079
<v Speaker 1>And the difficulty of servicing them twenty four to seven.

772
00:37:42.239 --> 00:37:44.639
<v Speaker 2>Yeah, and then what a twenty four seven knock actually costs?

773
00:37:44.719 --> 00:37:50.480
<v Speaker 3>Yeah, it's expensive and it the minimum the minimum number

774
00:37:50.480 --> 00:37:52.360
<v Speaker 3>of people you need to employ to do it properly

775
00:37:52.679 --> 00:37:56.280
<v Speaker 3>is high. Yeah. Yeah, and you just can't get the

776
00:37:56.280 --> 00:37:57.760
<v Speaker 3>scale like that, that's the biggest thing.

777
00:37:57.800 --> 00:38:00.800
<v Speaker 2>Like, yeah, if you're big enough in this debate, and

778
00:38:00.880 --> 00:38:03.000
<v Speaker 2>it is still a debate because now we get into

779
00:38:03.079 --> 00:38:07.159
<v Speaker 2>elastic utilization. Right when I had to build out websites,

780
00:38:07.199 --> 00:38:10.199
<v Speaker 2>we built for peak. You know, you had to have

781
00:38:10.320 --> 00:38:12.719
<v Speaker 2>enough gear to handle the peak load and then it

782
00:38:12.840 --> 00:38:15.920
<v Speaker 2>ran its sixty percent utilization if you were lucky and

783
00:38:16.079 --> 00:38:18.440
<v Speaker 2>you don't deal with that with the cloud, your peak

784
00:38:18.440 --> 00:38:21.239
<v Speaker 2>can move around and you you scale dynamically. You only

785
00:38:21.239 --> 00:38:21.960
<v Speaker 2>pay for what you use.

786
00:38:21.960 --> 00:38:24.039
<v Speaker 3>If you're a retailer and you're having to build for

787
00:38:24.719 --> 00:38:27.639
<v Speaker 3>Black Friday or peak, that's yeah, it's crazy.

788
00:38:27.719 --> 00:38:30.480
<v Speaker 1>Once you rather just moving on or let it do that,

789
00:38:30.880 --> 00:38:31.360
<v Speaker 1>not moving.

790
00:38:31.400 --> 00:38:33.199
<v Speaker 2>The knob moves by itself.

791
00:38:34.199 --> 00:38:39.599
<v Speaker 1>I can see it. It's moving. Yeah, it's not.

792
00:38:39.480 --> 00:38:42.719
<v Speaker 2>The same at all anymore. But yeah, it's been years

793
00:38:42.760 --> 00:38:44.440
<v Speaker 2>since I've said, you know, if you haven't already moved

794
00:38:44.480 --> 00:38:45.920
<v Speaker 2>the cloud, your CFO is looking at you're like, what

795
00:38:45.960 --> 00:38:46.559
<v Speaker 2>are we doing?

796
00:38:46.840 --> 00:38:47.079
<v Speaker 1>Well?

797
00:38:47.119 --> 00:38:49.320
<v Speaker 3>I think yeah, I think that's a conversation most people

798
00:38:49.360 --> 00:38:51.559
<v Speaker 3>had ten to fifteen years ago. Though, the conversation over

799
00:38:51.559 --> 00:38:54.559
<v Speaker 3>the last couple of years is what's your AI strategy?

800
00:38:54.639 --> 00:38:54.800
<v Speaker 1>Yeah?

801
00:38:54.880 --> 00:38:59.079
<v Speaker 3>Right, Yeah, every every boardroom is asking their your exact team, like,

802
00:38:59.079 --> 00:39:01.320
<v Speaker 3>what what's your ais ATEU? What are we doing to

803
00:39:01.920 --> 00:39:06.079
<v Speaker 3>uh automate everything through AI? And that's a much harder

804
00:39:06.159 --> 00:39:08.280
<v Speaker 3>question if you're not already on cloud. So if you,

805
00:39:08.400 --> 00:39:13.039
<v Speaker 3>if you're, if you, if you skipped that way, you're

806
00:39:13.039 --> 00:39:16.400
<v Speaker 3>already behind because the next thing is going to be like,

807
00:39:16.639 --> 00:39:18.880
<v Speaker 3>you know, what are we doing to automate things with AI?

808
00:39:19.000 --> 00:39:20.679
<v Speaker 3>What do we doing to automate sales? What are we

809
00:39:20.679 --> 00:39:23.880
<v Speaker 3>doing to automate marketing? You know, are the engineers and

810
00:39:23.920 --> 00:39:25.400
<v Speaker 3>our company using AI tools?

811
00:39:25.559 --> 00:39:27.920
<v Speaker 2>Most these tools are dependent on cloud. They dependent on

812
00:39:28.280 --> 00:39:30.000
<v Speaker 2>You need to have your data there already if you're

813
00:39:30.039 --> 00:39:31.280
<v Speaker 2>going to take advantage of That's right.

814
00:39:31.239 --> 00:39:35.239
<v Speaker 1>All right, good, we've beaten that horse. Is there anything

815
00:39:35.280 --> 00:39:37.360
<v Speaker 1>that we haven't talked about that you would like to mention?

816
00:39:37.960 --> 00:39:42.079
<v Speaker 3>Mostly just uh that you know, we love the fact

817
00:39:42.079 --> 00:39:44.599
<v Speaker 3>that Azure has been such a great partner with us

818
00:39:44.639 --> 00:39:47.679
<v Speaker 3>and integrating us into you know, in such a tight way,

819
00:39:47.760 --> 00:39:51.199
<v Speaker 3>into the experience, uh for customers who want to use

820
00:39:51.320 --> 00:39:53.559
<v Speaker 3>you know, the best vector database out there. We are

821
00:39:53.599 --> 00:39:57.400
<v Speaker 3>integrated already into Azures. So it's a phenomenal experience. I've

822
00:39:57.400 --> 00:39:59.800
<v Speaker 3>said that already, But like you're the number one.

823
00:40:00.320 --> 00:40:01.840
<v Speaker 1>Yeah, yeah, I love.

824
00:40:02.000 --> 00:40:04.159
<v Speaker 3>I love that we have such a tight partnership and

825
00:40:04.800 --> 00:40:06.639
<v Speaker 3>it creates a great experience for our customers and I

826
00:40:06.639 --> 00:40:09.119
<v Speaker 3>will keep make sure that people go out and try it.

827
00:40:09.119 --> 00:40:09.639
<v Speaker 2>It's awesome.

828
00:40:09.719 --> 00:40:11.679
<v Speaker 1>Well that sounds like the end of the show, So

829
00:40:11.760 --> 00:40:14.159
<v Speaker 1>thanks very much, Ken, Yeah, thank you for the pleasure

830
00:40:14.239 --> 00:40:16.159
<v Speaker 1>having you and talking to you. I learned a whole

831
00:40:16.159 --> 00:40:18.840
<v Speaker 1>lot and I'm sure listeners to too, And we'll talk

832
00:40:18.880 --> 00:40:42.639
<v Speaker 1>to you next time on dot net rock. Dot Net

833
00:40:42.719 --> 00:40:45.639
<v Speaker 1>Rocks is brought to you by Franklin's Net and produced

834
00:40:45.679 --> 00:40:49.519
<v Speaker 1>by Pop Studios, a full service audio, video and post

835
00:40:49.519 --> 00:40:53.679
<v Speaker 1>production facility located physically in New London, Connecticut, and of

836
00:40:53.719 --> 00:40:58.639
<v Speaker 1>course in the cloud online at pwop dot com. Visit

837
00:40:58.679 --> 00:41:00.800
<v Speaker 1>our website at d O T N E t R

838
00:41:00.840 --> 00:41:04.800
<v Speaker 1>O c k S dot com for RSS feeds, downloads,

839
00:41:04.920 --> 00:41:08.599
<v Speaker 1>mobile apps, comments, and access to the full archives going

840
00:41:08.639 --> 00:41:12.039
<v Speaker 1>back to show number one, recorded in September two thousand

841
00:41:12.079 --> 00:41:14.719
<v Speaker 1>and two. And make sure you check out our sponsors.

842
00:41:14.880 --> 00:41:17.880
<v Speaker 1>They keep us in business. Now go write some code,

843
00:41:18.239 --> 00:41:19.000
<v Speaker 1>see you next time.

844
00:41:19.920 --> 00:41:21.719
<v Speaker 2>You got Jamtle Vans