Hot Topics in Data with Ethan Aaron from Portable.io

We are kicking off Season 3 of What's New in Data with none other than Ethan Aaron: CEO of Portable.io. Ethan is known for founding Portable.io – a leading Cloud ETL product – along with his viral perspectives on data and "low key" data events that have taken over the industry with appearances in New York City, Chicago, Denver, Toronto, Boston, and many other cities. We chat with Ethan on the hottest topics in data including macro economic effects on the data industry, consolidation of the data ecosystem, and of course data contracts.

BONUS: Sign up for Portable.io's Low Key Data Conference: a free data conference featuring Fortune 500 data executives, leading data venture capitalists, and other expert data practitioners:

https://docs.google.com/forms/d/e/1FAIpQLSfUxFu0SXsAo3j75EZUib30M4hX8pMMguySiPcaVsPuknGxXg/viewform

What's New In Data is a data thought leadership series hosted by John Kutay who leads data and products at Striim. What's New In Data hosts industry practitioners to discuss latest trends, common patterns for real world data patterns, and analytics success stories. 

Transcript

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Hey everyone, we have a special episode of What's New and data today to kick off season three, I wanted to highlight a really great event coming up, called the low key data conference sponsored and led by portable.io. And their CEO Ethan Aaron, who's with me today. Ethan, how are you doing? I'm doing great. Thanks for having me. Always, always great to catch up. I love love these conversations, John, as always, oh, yeah, your your your frequent guests on what's new and data. We love having you and your spicy takes on data. Tell us a bit about your conference that's coming up. Totally. So if you think about the conference and event landscape throughout the year for data people, there's big events, there's like snowflake Summit, there's or DBT coalesce and they have different tilts like maybe one slightly more vendor B one's more list. One of the things that I've just realized is missing is a recurring cadence for people to just get up to speed on what's actually going on. And like there's there's LinkedIn if you want just like tidbits of information throughout a week or throughout the day, there is podcasts from you from Joe Reese from others that are more like on a weekly basis kind of deep dive with one person. The the gap that we're filling is great. If you just want to know if you want to sit down for four hours. Listen to 20 speakers, the top venture investors in the data world, the biggest spiciest influencers out there in the data world, the heads of data GitLab or Saks Fifth Avenue or lacework. You just have one place one day to go or watch the recordings afterwards. And just get up to speed like great, I understand what's going on for the next year for next quarter. That's the goal, the whole thing's four hours, it's entirely virtual, it's going to be jam. My job is going to be make sure we can get content out of 20 speakers in four hours. But it'll it'll it'll be nonstop. Yeah, definitely a who's who are people in data showing up and sharing their expert insights at your events. So we're all super excited for it. For the listeners, the link to the events, signup is in the description. But to really kick this off, Eric, Ethan and I are going to go through a few hot topics and data right now. And we're just going to get each other's perspectives on there. We're just gonna go in order. How's that sound? Ethan? Let's do it. Cool. So so the first question, everyone's talking about the macro, everyone's talking about the economy, and really how that's gonna tie back to the data landscape. So I want to get your perspective, what's the job market for data going to look like as the macro headwinds? increase a bit? Totally. So when I think about both the people, I'm talking to the conversations, I'm having the things that are top of mind for even like, whether it's our customers, or prospects, anyone, it is not their data stack. It is not the cost optimization, it is either their existing job or the next job they're trying to get. We're already seeing layoffs take place across the board in big tech and small tech and in startup landed in an enterprise world. And it's heavily impacting the data world, which sucks. There's a lot of phenomenal talent, that it's not, it's not their fault, but they're looking for new jobs at this point. There's also people in existing jobs looking for new jobs, because they know they start to understand the financial situation of their business, or they just, they just want to change. So to me, when I think about everything going on in the data world, right now, this is by far the most important thing to the industry and to the ecosystem. Unfortunately, what I think is happening right now, we are seeing layoffs, we're seeing a lot of people move. I think consulting firms are picking up more, both demand and also supply of talent and ecosystem. We're seeing an interesting shift where people in the data world, it's not as much of like, oh, what's the adjacent role? I can have as much as should they start their own consulting firm? Should they go work? Part time for three companies? Is that a full time for one? I think that's going to be one of the most interesting things to see this year is how many of these people go back into the traditional workforce as a analytics engineer or data analyst and how many people just say, I'm gonna go I'm going to do this myself and, and go sign up some some smaller roles that add up to more money. So that's my take on the job market right now. What do you think? What are you seeing out there? Yeah, you know, it's always sad to see layoffs. And you know, jobs be impacted, because ultimately we know that there's a lot of talented people working on data. One thing I will say is that, you know, might be a tale of two cities, so to speak, because on the other hand, I do see huge tailwinds and growth in enterprise digital transformation. I see teams actually doubling down and growing, they're on the enterprise side, because we are in a very, I would call it deployment phase of Cloud Analytics at the enterprise tier. And ultimately, a lot of budget has already been allocated to transforming enterprises to be data driven. Cloud first. Org anomic. So, you know, it's going to be interesting to see, maybe it's it's kind of a, you know, you know, we're seeing more investments into certain areas, and, you know, data engineers have to figure out how to align themselves and industries where data is, is becoming a big focal point for growth. Yeah, totally. Yep. 100%. And, you know, that just comes to our next point, which is as, as a data engineer, as a data practitioner, the key is being in the right place at the right time and bringing value. So like, how do you differentiate yourself as an employee, or even as a consulting shop, maybe even a company or a group within a company? Just the the key question here is, how do you stand out and show that your unique value ROI? Back to the first point of what's happened in the job market, I don't view it as demean I don't view it as demand for data talent is decreasing demand globally across the board is increasing. It's a reallocation and save. And my take is tech is the same way. Sure, Google does not have as many employees as they did before. But there's a ton of phenomenal talent that's been reallocated and going to find, and going to lead engineering functions and other business functions at tons of super cool, medium to long term companies. So that's the macro point. In that world, though. You want to be something like you have to be selfish, you have to think about what's best for you as an as a person, are you as a company? Are you as a consulting firm? And a lot of it part of IT skills? Like I know, this is like part of it's hard skills, like you know, SQL Do you can you deploy your infrastructure? Can you use the tools, if I but another part that I think a lot of people in the dental world overlook is how do you get in front of people? How do you differentiate? How do you actually stand out in a really tough market where there are a lot of other people also saying, hey, I want to I want to be the next head of data at your company? How do you how do you stand out my few recommendations there, number one, is find a way like pick the skills that you want to show off, maybe it's like, Great, I'm great at standing up data technologies. And instead of going and be like putting it in bullet three of a resume, I would think about LinkedIn, put it out, put it on LinkedIn, make it front and center, put in the header of your profile, because for me, whenever I am interviewing people, I don't just look at their resume, I look look at their LinkedIn profile as well. So that's number one, number two videos, till it right now. See, I'm sending someone a resume versus sending them a 10 minute overview of you standing up an entire data stack, or you actually walking through a pipeline or custom script that you built, you can reuse that you can reuse it, you can send it to 10 different people, it's a better version of a cover letter and 2023. So that's number two. And then number three is, if you're not a podcast person, don't start a podcast if you're not a LinkedIn person, or LinkedIn, but find somewhere where you can start engaging with the community. Maybe it's Reddit, maybe it's Quora, maybe it's somewhere else and start becoming more vocal. Because that brand, like when you think about when I when I see someone on LinkedIn, saying I'm looking for for a job, if I've never seen him on LinkedIn before, I want to help like, I will take meetings with anyone looking for rolling data right now, like putting me on LinkedIn, have a great time. But if I see someone who I also recognize from their content every day, it's like, Oh, I feel like I know this person. So starting to build that ongoing brand. And awareness is also really important. So it's really just like, the other three, my biggest recommendation, though, is create a 10 minute video, create a five minute video, it's just like, This is how I can add value. This is me showing you that I can communicate about it that I can like, do the things that I tell you I'm doing and use that as your cover letter. It's it's a lot simpler, and it makes you stand out. What's Yeah, yeah, incredible advice. Ethan. So in to your point, you know, not everyone you know, just wants to make a LinkedIn not everyone wants to make, you know, whatever that next popular social media outlet is, but everyone can find something that's right for them. So it might be maintaining a GitHub repo might be contributing to other popular open source projects that already have like 1000s of stars and you're opening up pull requests or opening up issues and starting to build credibility within the community that way and then I love the idea of just you know, making a video as a as the modern cover letter to show hey, I can I can communicate the value what I'm working on because ultimately when You are working for a data engineering team, that data engineering team on top of building pipelines and maintaining them to consumers and all that technical work is at the same time advocating for their role by talking about the ROI that they're bringing to the business, right. So that that's always going on in parallel. So having another person on your team who's good at that sort of communication, good at building that case for themselves for what they're working on. That's just a very invaluable skill. So yeah, I think incredible advice from your end, Ethan. Sweet. So I got I got I got, I'll take question three at this point, you just brought up, you brought it up. There's data producers, there's data consumers, there's a lot of talk in the market around data contracts, and kind of how these people should interplay with each other. The pros, the cons, is it the right interface? Whose fault is it? Is it someone's fault? what's your what's your take on data contracts? I know, I think in data contracts are already everywhere. They're just very implicit. So, you know, people aren't calling the data contracts, they are, you know, reading all the pop, I mean, some people are reading the popular blogs on the topic. Chad Sanderson has done some incredible out there and you think your, your, your takes on it are also very widely covered. But I see lots of enterprises, lots of data teams, you know, doing some form of data contract management, having SLAs to business consumers, it's just a little implicit, right? It's, uh, hey, you know, I'm getting escalations right now that, you know, our data is not being delivered in this format within an x minute SLA, right? But that SLA isn't codified. It's not reified in the pipelines and the warehouse. So I think it is actually something for teams to look at, to actually start building internal collateral specs, code that talks about the specific data contract. facet. Yeah. Yeah. And, you know, I think the, the next big question that we get are, you know, should should producers or consumers make the contract? So my, so my take on this is a few fold. So if you think about the problem with data contract solving, in a nutshell, it's teamwork. Like if you think about data contract is an internal thing. There's external data contracts, which are typically either API, their API's in some capacity, whether it's a REST API, an s3 fault, like some interface for people to interface with, inside of a company. I think a lot of interfaces fall under the bucket of teamwork, it all comes down to how do you work together as a small team or as a really big team or an organization. So not Well, I don't think it's one person or the others job to solve the problem. It's not the producers job to perfectly maintain thing, it's not the consumers job perfectly maintained. Just like in any relationship with a co founder, same thing. If I was just like, hey, Azim it's your job to make sure that we talk correctly, and that we interface correctly. That's ridiculous. If my job was the same thing. It'd be ridiculous like the it's really in service of teamwork and a vision. So I think it contracts are very good, formalized interface for production data pipelines. But if you think about the problem they're solving, I think a lot of the stuff that resonates in the conversation around data contracts right now is really just friction that exists in data teams. And there are things beyond just data contracts that can help with that, like setting a joint vision of like, hey, doesn't really matter what the interface is, we need to figure out jointly between me and data engineer data engineering analysts, like how many customers we have, I don't care what the API interface is, in the middle, we need to know how many customers we have. So like sitting in joint vision is one way of addressing it. Open communication, like you can probably see, if you have a small enough team, or a small enough number of stakeholders, you could probably remove a data contract and just replace it with weekly meetings with between data producers and data consumers. And it's simple. It's again, it's teamwork. So I think with any teamwork, there's communication, there's process and there's tools. There's probably other stuff. So I think about it as one of the one of the tools in the tool chest. So it's called toolbox. I think some houses too, which is sad, I should know, it's, it's still. But that's how I think about data contracts. It's a tool in the tool chest, the toolbox of teamwork as a data function, I think that's going to be a lot more complicated going forward, because people are going to realize they can't just carve themselves out into a box and say, All I do is this because teams are shrinking means you have to take on more responsibility, it means the teamwork dynamics are going to shift. And I think the interfaces and tools have to change accordingly. So I think we're gonna see a lot of change here. I think it's gonna cause a lot of pain and a lot of friction. And my biggest recommendation is just be open to using as many tools as you can to accomplish things as a team. So So this is my big advice when it comes to data contracts. data producers data teams should know that there is already a contract, it's just implicit and no one's talking about it. And if it's implicit, it means the consumer is making the contract. So as a data team, you know, and I, we put this under the bucket of data producers, because they're the ones serving the data to the business as pipelines. So, you know, you need to be proactive, you need to evangelize the role of data contracts in your business. So you can say what's technically possible with the resources you have, because it's possible that the, the SLA is in the expectations of the consumers. And by consumers. I mean, the business users of the data, and ultimately, the business users of the data, in many cases, are the ones advocating the budget, they're the one pulling in the enterprise architects, they're the ones who are, you know, making the actual big purchases. And then the producers are the ones who actually take over those investments and build them out, you know, take take the products and build the pipelines. So the producers have to basically take what's, you know, sitting in front of them, you know, the the tooling, they have the budget, they have the amount of cloud capacity, they have all that stuff, and based on that, make the contract based on what's possible, because otherwise, you're gonna have consumers who are saying, Oh, well, we thought we were going to get real time data. Well, you know, the producer can say, we don't have a streaming tool, the consumer can say, like, hey, you know, we want we want data mesh, we want this and that, and the producer have to say, Okay, well, we don't have the the governance or the Federation, or whatever, layers that are required to actually build what you're looking for. So this is this is my take on data contracts. I think ultimately, I think it is a useful for, you know, mental model, and, you know, organizational alignment framework, that data team should really try to advocate. So, so, you know, I think everyone's talked about data contracts, they now have our views. Do you have any last thoughts on that one before we move on? Okay. Okay. So this is this is going to be a hot topic. So we are we obviously know, there's been a ton of momentum for data startups, the data industry in general over the last five, six years. Matt Turks mad data landscape, when you look at that, that's a that's a popular one. It's just so many startups in there. Now that you know, you need a magnifying glass to look at it at this point. So he did actually add zoom functionality to the Mad landscape on his website, which is a good feature. Yeah. But do we think that data companies are over capitalized? Yes. So here's the here's my take on what's going on right now. And started to talk about this, because it's so new. And luckily, it seems like everything's kind of okay. But we'll see is Silicon Valley Bank last week? What happened? Like, to best my knowledge, the company was fine. Like it was run efficiently, like great team, awesome partner for startups and venture funds. Problem is risk management. And risk isn't just like, Did you do the right or the wrong thing? It is, what do you believe the future is going to look like. And I think, one year ago, two years ago, three years ago, everyone believed the future will include low interest rates for an extended period of time, when you make a bet that the future will include include low interest rates, you can assume that valuations will be quite high, and they will remain quite high. And therefore you can afford to step up and higher and take on more money, because at some point, you'll be able to get that money back in a public market, or through an acquisition at a high multiple, because rates are so low. So I think what we were seeing was a lot of traction when traction is the wrong word is my is my take on this is we were seeing a lot of noise and a lot of capital being pushed into data, but also into the tech world. And as a whole, because rates are so low, you can justify valuate with super, super crazy high valuations. Some might not have been notified. But you could do it because rates were zero, effectively, or very dark, close to zero. Today, that's changed, and nothing else, like ignoring everything else, when rates go up, like multiples and valuations go down, and they go down pretty fast. So I think what's happening today is a lot of people, a lot of startups as well as banks, and everyone else, kind of were under the assumption that the future was low interest rates, high valuations, whereas that's not the future we live in today. And I think it's gonna lead to, we still have great technologies out there. We still have great products and great business teams, but I think kind of like what we're seeing with SBB its cap tables might be a little skewed right now, they might not be set up in a way in which common stock is worth anything or employee equity is worth anything, or the investors are gonna get a great return like they would have hoped to keep doubling down on an investment because it's gonna become a $10 billion company. that company that a year ago that they thought would become a $10 billion enterprise might now only have a trajectory to get to$500 million in, in value. So that changes everything, or the company's fundamentally flawed, a lot of them are not. A lot of the cap tables, I think are is where a lot of the interesting things are going to take place in my mind, in the in the tech world and in the data world, because your investors get their money back first. That's how a lot of stuff works. It's convertible notes. If they put in $100 million, and you sell for $99 million, they're gonna get all that $9 million to your business. So like, I think we're I think we're gonna see a a real, not real happy allocation, reallocation, I think one of the biggest things that's going to be negotiated and thought about going forward is, okay, great, people are gonna realize they are over capitalized that their cap table does not make sense to incentivize employees, founders, investors did really keep doubling down. And it puts people at like, people have to sit down at the table and be like, how do we set this up? How do we reset everyone's expectations? Because a lot of the expectations that were set last year, will not be met, is my take. Yeah. And Ethan, I, I'm not sophisticated enough on that particular topic to debate you on, you know, capitalization and markets. I know, you have a bit of a New York, Goldman Sachs type of background, I've always been a Silicon Valley computer science guy, you have a bit of you have both, obviously. But you know, so I can't debate you on whether data companies are over capitalized or not. But my question for you is, okay, let's let's just, you know, let's just take that argument and say, let's assume that data companies are over capitalized. What does that mean for the data practitioner? I so I think I think a few fold I think there's two things pushing down on data practitioner, let's let's instead of calling a practitioner, I'm just going to pick a title, let's say it's a data analyst. Is it like the data analyst at a small company, you effectively do everything analytics related? Sure. There's two things that are taking place, data companies are not the only ones over capitalized. So the thing that you really care about is you don't care about your vendors, you care about your job. So a lot of companies are going through the same thing, those their e commerce companies that are also over capitalized, and they're looking at their company being like, okay, great. Can we cut back on Facebook spend, then it's like, okay, great, we have to cut back here, then it's like, okay, great, we might need to downsize our data team. So it's like, I think that's the first thing that's going to hit data practitioners, they are not going to care about the vendors, they're going to care about their job, first and foremost, second, and that it's the same dynamics apply, like higher interest rates, valuations and expectations go down, like phenomenal companies, like Stripe, having layoffs, like they're phenomenal business, but expectations are being reset. So that's the most direct part. In terms of vendors. I think we're gonna see a lot of business models that people thought would work in 2021, and 2022. A lot of like, very long term r&d, like more focused on GitHub stars and revenue, more focused on Slack community membership than customers. I think I think people are gonna get impatient. And I think you're gonna see a lot less free stuff for the world, and a lot more vendors out there competing with each other to try and take more revenue, or starting to find a way of being like I charge you$100 A month last year, can I charge you 140, maybe 200. So I think what you're going to start seeing is more competition for dollars, because these companies expectations are being reset, but like, they still have to grow. And I think people are going to become impatient, because they're not going to be like an investor is not going to sit down with a company, that's a unicorn and be like, Let's reset everyone's expectations. And by the way, go keep focusing on free stuff for the next three years. Like you can't write off$100 million investment in a company and not expect it to start generating revenue fast, you can only make so many. We only have so much time. So I think I think the two dynamics play for data practitioners, number one, their own job, that's the top priority for everyone. The second one is it's not prices are gonna go up holistically, but I think we're gonna pretty rapidly figure it out in the next 18 months, figure out which products in the data world people will pay for and which ones they won't. And I think the ones that people will not pay for are gonna have a lot of problems. And I think the ones that people will pay for, we're going to pretty rapidly figure out kind of what those price points are because vendors are going to be trying to figure out what that price point is with their with their customers because they have to they have they need to they need the revenue to justify the or to try and justify the valuations. They were there at last year. So okay, so I'm a data analyst. I'm a data engineer. I'm hearing anecdotally rumors that data companies are over capitalized. That makes me want to To, you know, evaluate the vendors that I have, and really go back to which ones are creating the most like business value, right? So you know, which one is actually helping me move the needle when I communicate to my boss, or my boss is, you know, communicating to the executive level, what we're doing with data and how it's serving the business, you know, you want to be able to say, you know, which vendors are helping you the most, which ones are probably maybe superfluous, or you're talking about, hey, companies consolidating, but teams can consolidate their own data stacks, right? I mean, they can they can, they can find a way to, rather than having a few specialized tools, they can have more, you know, end to end tools, or, you know, if it's, if it's just like a tool that's being used for like a fringe use case, you know, those are the ones that you're going to want to kind of evaluate. But, you know, I think this is a good segue into our last topic, which is, you know, biggest predictions for 2023. Totally, you wanna you want to kick this off? Yeah, sure. So, you know, I brought this up before, but you know, I would say, you know, taking these, these data pipelines, these projects, lots of data being accumulated in the cloud. Now, everyone has tons of raw staging models where data is being loaded in. But, you know, it's probably 10 to 15% coverage of how much of that data is actually being used for operational use cases. And when I say operational use cases, it actually ties back to that business's customer base, how they're making revenue, either back end operations, or customer facing operations, like maybe you have some data driven workflows that are, you know, communicating with your customers are part of the revenue flow. So I think it's time for for more coverage of the amount of data you're accumulating to be pulled into the operational side of the house. Meaning that you're, that you are really using it for your your business use cases, less of an emphasis on ad hoc reporting. And that also will is going to bring in like an element of you know, consolidation, because you're going to pick the data products and platforms, you're using, the ones that are really going to move the needle the most in terms of pulling it into the operational side of the business. The other. Yeah, go ahead. So before I go into my prediction, the interesting thing there is, when I, when I talked about data, there's three ways to create data, create value from data, one, analytics is dashboards, insights, things to make better strategic decisions. That's where a lot of data teams start. Second one is operations, what you just talked about streamlining manual tasks. Third one is build products and people pay money for, that's blurs the line between data teams, and product teams. And then the fourth one is out how you create value, but it's like risk mitigation, security, privacy, data, ethics, etc. In that second bucket, what you're talking about here, where we start blurring the line between analytics and operations, which I do believe will start happening, especially with visualization tools that are mixed with credit platforms like ritual, I think those lines start to blur. But the one thing to note there, when you talk about consolidation is it's not just tools. In a lot of companies, those are two fundamentally, if not more fundamentally different teams of people. A lot of company like when I was a librarian, I was the head of business intelligence. My job was built dashboards and analytics, we had a different team whose full time job was operations and automation. They used different tools and different pipelines and different everything to connect our CRM system to our HR system to our billing system. And I think it'll I believe, over time, these will converge. And I believe we'll have one data team that does everything, using different tools for different things, sometimes sometimes being able to collapse them. But I think there will be an interesting team dynamic there as well. We're someone whose full time job used to be connect systems is now sitting next to it, an analyst who saying hey, can we reverse ETL that data or just stream it directly from the source? And I think you're gonna have these people that have different OKRs different KPIs, one of which is build dashboards, and one of which is automate workflows sitting down and trying to collaborate and think about being a team like we were talking about with theater contracts. So I think that's going to be an interesting dynamic as they you can tell it, but I agree that those those will be consolidated. Yeah, I think that this is really going to be a you know, I think one cicada from data dot world calls it the Show Me the Money year for for data so shout out to one for that line. But you know, ultimately, I think it's it's true across the board right when it when you talk about consolidation and and I think that we're also seeing more of the data platforms that were more focused before starting to spread their wings and or to overlap with with other, you know, even partners. Right? So we're seeing some competition. Yeah, this. So if I if I, if I hadn't met in 2020, maybe it's too late, maybe it's already happening. And this doesn't sound exciting, but I think a lot of the parts of the people that partnered to get where they are today is multibillion dollar 10s of billions of dollars worth of companies, companies like snowflake, 510, and DBT are not going to be as friendly as they were before. Maybe this is a spicy take on the market. But even as recent as today, snowflake announced they have a native ServiceNow integration into snowflake. They already have a Salesforce integration I posted a few weeks ago, predicting that snowflake was going to have and you might know more about this than I do native like Change Data Capture capabilities for certain types of data sources. And someone that snowflake responded saying they already have something in that realm. I don't think it's quite the same. But so I think that dynamic is going to be very interesting to watch the the data warehouses specifically snowflake relative to the to five train when it comes to the ELT world. Because if someone's paying $100,000, to snowflake, and 100,000 hours to five Tran, if you're five trainer, you're gonna build a data warehouse, that's, that's quite difficult. If you're snowflake, you're gonna build a ServiceNow collector to try and take 10,000 of that $100,000 Like they're doing it. So that's one thing, I think you're seeing a very similar dynamic right now between five training and DBT, where five Tran is offering DBT core transformations inside of five Tran. And again, it's one of those things, where is it super straightforward for someone like DBT to build five Tran? Not necessarily. But if five journalists use DBT core, it's open source. So they're able to do that. So and if you rewind back three years ago, five years ago, those companies were three of the closest partners in the ecosystem. And I don't think those partnerships are gone. But I think going back to what's going on in the financial market, people have to make money. And they have to, they have to make moves that will allow them to make money as quickly as possible. And I think it's gonna lead to a really interesting, I think, at the biggest companies, we're gonna see more conflict, we're gonna see more things where people start stepping on each other's toes. And then I think you're gonna see a lot of the smaller companies starting in partnerships to compete with those bigger platforms trying to do it all on their own. Because if it's, Hey, you can do everything natively in one platform. But it's not as good as for small companies that are seamlessly working together with newer technology. I think that's, that's my biggest prediction. I think it's not just those three companies. But I think a lot of the bigger companies are going to pretty rapidly expand scope, either through mergers, like m&a, through in house development, or through through just Yeah, so it's, that's my take is, is we're gonna see more conflict than we've seen in the past up until now, everyone's been perfect friends. I don't think that's going to happen anymore. I think it has to change. Well, we're all going to see it play out. So should be yet another exciting year in data for us. Ethan, always great to have you on the show. Always good to be here. Yeah, Ethan Aaron, CEO of portable.io. This week, at the time of recording, hosting the low key data conference link to the conference will be in the description of this pod. It'll be live, but also available on demand for watch later, lots of great data experts in the industry, data leaders, you know, fortune 500 companies going to speak and share their expert insights there. So, Ethan, that's gonna be super exciting. Totally. We're definitely looking forward to march 22, one to 5pm Eastern, free to sign up, go to portable that IO. And you're gonna drop a registration link, as well, but super excited for people that attend. Again, not to mention that it's free. That's that's that's another great aspect. Just the communal sharing of expert Insights is just another great thing that you're bringing to the table. Ethan and I also recommend everyone follow Ethan Aaron on on LinkedIn as well, if you want more of his takes on the industry. Well, thank you, Ethan. We'll we'll definitely be in touch. Totally talk to you.