Advancing Data Integration and Analytics with Sam Pierson and Ori Rafael of Qlik – Tech Talks

We are at Click Connect 2025. And one of the big announcements on the keynote stage this morning was the availability of Click's Open Lakehouse, which sort of bridges the gap between data lakes and the traditional world of data and analytics. One of the things that's making this happen was the acquisition earlier this year of a company called UPS Solver, and I am thrilled to be able to get a little bit into more detail here.

Uh, welcome. Uh, it's good to have you tell us a little bit about yourself. Thank you.

My name is Ori. I am was the co-founder of Olver and the CEO and coming from a data space, eventually I'm a data engineer. Excellent.

Well, it's nice to, it is nice to have you here to go into a little bit more detail about this. Yeah. And what's your angle?

Yeah, so I'm Sam Pearson. Um, I oversee the r and d teams for the data business unit looking after all of our data related products. And, uh, yeah, I was super excited this year to be able to have Ori and and team join us.

Uh, just a just been a fantastic fit so far. Well, one of the things, uh, the whole industry is looking at is how to make better use of data lakes, meaning, uh, we've got these things there. Not always very, uh, uh, structured.

They're not always very easy to use. Uh, and yet people just keep pouring more and more data into them. Uh, Apache Iceberg is everywhere.

Uh, what is the difference in using this new open Lakehouse as opposed to a traditional data lake? Yeah, I think, um, look, I think there, you know, you, you hit it right on the head, right? Is like the, the data lake was something where, you know, it was just put all of this data inside of it, right?

As sort of the wild west. You have the other model, which is the, the data warehouse, which is, you know, highly structured, very governed, right? And the, the open Lakehouse model, and kinda how we're thinking about it is that this is something that is sort of marries the best of both worlds, right?

Where you can put data in there, but the, the ease of use in terms of the transformations and what you're able to do with that data really unlocks it along with a lot of like, uh, efficiency and cost benefits. And your team, uh, I guess hit on a, a great way to make data lakes more useful. Mm-hmm.

Uh, tell us a little bit more about that. Well, as Sam was saying, it, it's in the name, the benefit of the lake with the bene, which is cost and openness with the benefit of the house. So the warehouse.

So we have seen this motion happen in the past. We kind of, we've been waiting for that to, to actually succeed. We had a OOP and everyone who kinda rushing to a OOP as the data lake that's going to replace the data warehouse.

And what happened? It sucked. It was slow.

It was hard to manage too much engineering. It was a cost center instead of a value center. Now the new Lakehouse today is supposed to deliver on what we wanted it to, to actually do.

Iceberg is a major part of it. So in order to create data in the lake that people can actually query and use, you need to create a standard. We as AB solver are doing the same thing, have the same vision as we did in 2019, but we didn't have the standard that allowed us to bring us to bring the data to all the warehouses.

Today we have it. What do we do that's special? We are cre we are creating that house value in the lake.

We take care of performance. All the nitty gritty file system thing. What do you care as a user?

What's your file size or how many files there are or how are you gonna structure them or compression, like all of those nitty gritty stuff are not supposed to be burdened on the user. We are supposed to solve them as a vendor. But as you go to the lakehouse that's no longer the warehouse responsibility, it became the customer's responsibility.

So we, as a data integration company, we write data and we can't just take the position, we don't care how they consume it. By adding olver and its iceberg management capability to the portfolio, we are leveling the lake performance and ease of use with the warehouse performance and ease of use. That's, And it seems to me exactly right there, that performance is one of the keys here because it's always been hard to, uh, put data into a data lake in a, in a way that is gonna be useful in the future.

Uh, ingesting massive amounts of data is something that I'm hearing about from a lot of companies in the data space. And you talked to me, uh, and the tech field bank crew yesterday about basically, uh, ingesting just tremendous amount of data in near real time in order to build applications that we never could build before, right? Mm-hmm.

I, I agree. We as olver before click, we started only with big data workloads. The reason is that there is such a, a variety of tools for the small data, and we wanted to find, uh, some white space.

So we went for the big data. What does the big data customer want a data lake? Because the data is so expensive.

We as app absorber in the beginning, had the same challenge. This is why we went to a data lake. But once you make the data lake easy enough for the big data, why shouldn't all the small data be spilled there?

So I think we are now in the point of time where people are just saying, the lake is where we are gonna store all our data, and not just big data. What App Absorber is doing is adding those big data, really like truly big data capabilities into play and allowing the customers to enjoy the lake as if it's in the warehouse, by the way, not just performance, because you can put an engineer to solve your performance problem. The question is how long would it take?

How much they're gonna slow down your, your data consumers and everyone else? And is it going to happen all the time? Like, is are you gonna create a data engineering bottleneck?

Is it, I was a data engineering bottleneck and that's exactly what I'm trying to solve. So when Qlik was looking at UPS Solver, uh, what was it about the solution that appealed to you? Why did you think this, this is a Click company?

Yeah. This is a Click product. Yeah.

Well, I would just say, you know, in the, across the whole portfolio, um, the Iceberg Project, other open source, uh, open table format projects that we've been looking at, like, we really believe that this is a trend that's not just something that's kind of just starting. We're seeing actual, like, real production adoption from our users. And so this is something that we've had started to invest in, in terms of our own portfolio across Click Talent Cloud, across Talent Studio.

And one of the, you know, one of the things, you know, among many that we liked about UPS Solver was just the, the high performance, the scale of, of what you guys were able to bring in, in terms of ingestion, right? And I think, uh, for, you know, as we looked at our portfolio, the way that we have structured our technology, the way that we've looked at our different use cases, it's like being able to, being able to add on these different sources, bringing in different technologies for bringing data into the platform, allowing customers to make those choices. Uh, it really was a, was a great fit.

And, and again, just the, the fit in terms of technology, like this is something like we close the acquisition in January, like we're doing internal testing next week. We've got the early access program going now, and we've got customers signing up to give it a shot. And so all of those things were, uh, were fantastic.

The only other thing I would just say is, uh, as we, you know, we sat down with the team at the end of last year and we had these sessions. We were talking about, Hey, where are things going? What do we see happening in the industry?

There was just a real alignment on the vision and the strategy for what we see happening in the data space overall. And so I think it's been a, been a fantastic fit for us so far. Well, It seems like we're great fit for the Qlik Talent Cloud especially.

Yeah. Because, you know, I mean, you've got such a, an incredible capacity for data and having this, uh, new technique to get more data in there quicker, it really helps customers. A another thing that, uh, I think it helps is with, uh, partners like a WSI talked to them, uh, earlier today.

We had them present at our tech field day sessions. Yeah. And, uh, I'm sure that they love the fact that everything you're doing is based on openness and standards and that it integrates so well with the other, uh, aspects of the AWS solution that customers are using.

Look, AWS is also adopting Iceberg across like a large portion of their portfolio. So I think there's gonna be, uh, you know, we've been, you know, behind closed doors talking about different integrations for QTC with, uh, the, the various Amazon products. So I think, um, you know, it's a, it's a really strong fit.

They share the same vision around openness and choice. And again, I think like a couple years ago before this, right, I think there was more, more lock-in, more concern about vendors. And now it just seems like with these things being decoupled, it gives users a lot more choice.

They can manage their data, they can look at their metadata, they, they can look at things like, uh, uh, you know, the compute that they're using. They can look at the storage and they can store these things in, you know, in very, very tailored specific ways that allow them to really turn the dials on, cost performance, meet all of their SLAs, which is, which I think is, is great. Another thing, um, I wanted to ask you specifically now, as somebody who came from, uh, I was, uh, started my own company and, uh, we joined a bigger company.

Mm-hmm. Uh, one of the things for me as a, as a founder, was basically looking at it as a new toy box. Mm-hmm.

Like all the new capabilities that I would have that I could bring to what I was doing that I never could have achieved on my own. Mm-hmm. You coming into a big established company, like click, uh, what were the toys that you wanted to play with, uh, in order to better help your customers?

I think that the first and immediate store you start with what hurts you the most. Connectors, like we had a very good engine with very limited connectivity. I've lost so many deals on the variety of connectors.

Click has a huge variety of connectors, but also the hard connectors that are, you know, the mainframe, the SAP, the on-prem CDC, like the things that are very hard to get in the market that some people say they do it, but very few companies have been doing it for so long and so well they click. So that's the first thing I wanted to put my hand on. And you see it like in this launch, milestone one, all QTC sources.

That's the first thing we, we added. I think that the second thing is that Olver is a system that doesn't require too much engineering. It's declarative.

You declare what you wanna do, you don't need to specify what are all the right and left turns you're gonna take. Just set the, the goal. That's very good if you wanna go right to the business user and start your process from there, instead of building something on, on in engineering.

And Qlik has an analytics product, an analytics and AI product with a lot of customers and a lot of partners. So that's the toy box I want to go fish and connect our integration capabilities into. That's a lot of fun, isn't it, to be able to, uh, to do things you never could have achieved yourself, simply because it requires scale and experience and, and just man hours to, to make it happen.

Yeah. Mm-hmm. Mm-hmm.

Wow. Uh, so what, what's next? Um, if companies are excited about what you're doing, uh, with this technology, uh, can they try it?

They can try it, they can sign up to try it and they will be able to try it very soon. We launched today, we are gonna start, uh, a closed preview in the next couple of weeks. And, uh, after that we'll open it for ga not long after that.

That's the infection. Yeah. And if they're interested in, in, in other, uh, other things, uh, what, what do you got for them?

Yeah, No, I just think, uh, you know, if you're, everyone is, is learning about iceberg, you know, we've got customers who are still sort of trying to figure out what's what. com where you can go, you can read the white papers, you can read sort of the how tos all the way into some more, uh, more advanced topics. So definitely, you know, now is the time to learn, figure out how this is going to apply to your data strategy and uh, come talk to us, sign up for the early access program and get on.

Excellent. And, um, if you wanna learn more about this as well, we did record a deep dive technical, uh, tech Field Day session, uh, here at Click Connect. We're gonna be publishing that very, very soon.

com, the tech field, a YouTube channel, or check out the tech field, a plus YouTube channel, which is the new name for where you'll find interviews like this from Click Connect, as well as lots of other, uh, behind the scenes and, um, you know, more fun kind of content. So stay tuned for that, and we will bring you more from click Connect going forward.