Building Interactive Dashboards with Real-Time Data: A Conversation with Joe Karlsson

Episode 36: Building Interactive Dashboards with Real-Time Data: A Conversation with Joe Karlsson

In this episode of our podcast, we sit down with Joe Karlsson, an expert in building interactive dashboards with real-time data. Join us as we explore the world of real-time data dashboards, discussing their importance in decision-making processes and uncovering valuable insights.

Tune in to gain a deeper understanding of how real-time data dashboards revolutionize data visualization and drive informed decision-making

You can also listen on Apple Podcasts, Google Podcasts, and Spotify.

What You’ll Learn in this Episode

  • Importance of real-time data in decision-making processes.
  • Definition and purpose of interactive dashboards.
  • Key components of interactive dashboards: data sources, visualizations, filters, and interactivity.
  • Tips for designing visually appealing and user-friendly dashboards.
  • Overview of popular tools and technologies used for building real-time data dashboards.
  • Challenges and strategies for handling large volumes of real-time data.
  • Real-world examples and case studies showcasing successful dashboard implementations.
  • Future trends and advancements in real-time data dashboards.

Get in Touch with Joe Karlsson


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If you enjoyed this episode, check out this episode where we discuss 5 dashboard mistakes to avoid.

Episode Transcript

Generated automatically – there may be some errors.

[00:00:00] Hana: On today’s episode, we have a guest Joe Karlsson.

[00:00:03] He’s a software engineer who will be talking to us about building interactive dashboards with realtime data. Before we jump into the topic, can you tell us a little bit more about yourself,

[00:00:12] Joe: Joe?

[00:00:13] Yeah, I am Joe. I’m a developer advocate and software engineer. I recently got laid off and I got rehired by a new company called Tiny Bird.

[00:00:21] Yay. Congrats. Thank you. I I’ve been mostly like in the data space. But more and more recently I’ve been focusing more on like how to use data on the front end. And like doing realtime dashboards in particular, like my focus recently has been on realtime data, like how that works. And then today I, I’m hoping to talk a little bit more about like how to use real-time data effectively.

[00:00:45] And I think that that brings us to, to you and I also, I I’m on TikTok if you guys like TikTok and like want that stuff too. And I’m also on Twitter too. It’s just my first and last name, Joe Karlsson.

[00:00:56] Hana: I actually connected with Joe on TikTok. He has very wholesome content on TikTok. Very inspiring. I feel like , your videos are very motivating and so friendly for people who are in tech or wanna get into tech. So thank you for. Putting out this kind of

[00:01:09] content

[00:01:10] Joe: podcast listeners at home can’t help when I’m blushing right now.

[00:01:13] So thank you very

[00:01:14] Hana: much. So yeah, I, I’m really excited for today’s topic because I do have some questions and experience with realtime data and dashboards and I haven’t done them successfully, so that’s why I’m excited to talk to you about them. They’re hard. Yeah, that’s basically the gist, right?

[00:01:28] They’re hard and that’s why we’re so ex excited to have an expert like you on the show today. Can you first tell us, for those who are listening and are interested in. Creating dashboards with realtime data is how this can be used to improve user engagement with their

[00:01:41] Joe: dashboards?

[00:01:42] Yeah. Well, first of all, I think we have to define user because there’s, in my humble opinion, there’s two different users of like a realtime data dashboards and notifications, and that’s like end users of your product. But the other user is also like internal usage. It could be engineers, it could be like your chief board wanting real-time sales data.

[00:02:03] It could be an engineer looking for real-time like monitoring, logging data, but their systems. Or if like you’re a customer of an app and you want like a real-time dashboard of like, I don’t know, like where your package is being shipped around the world or whatever, right? Like or where your Uber is as it’s being delivered to you.

[00:02:20] So did I answer the question or did it make them more complicated?

[00:02:23] Hana: That makes sense about identifying who your target user of your dashboard. I’m glad you brought up this point, because usually when I advise people who are creating dashboards, the first step I always recommend is identify who your target audience is.

[00:02:33] Yes. Be a bunch of people looking at it, but who is your target audience? So let’s say someone identifies who their target audience is. Let’s say it’s their managers or another team once they identify that how can they use their realtime data in their dashboards to help with their engagement?

[00:02:49] Joe: I actually just talked with somebody this morning who built a real-time dashboard at their company. They do like real-time personalization for an e-commerce shop.

[00:02:57] And they built an internal tool system using retool. And the company worked for a tiny bird to build out a system. And the reason is, is because their bosses kept coming to them, asking them for updates about what are the sales for this quarter and what are our outages and all that stuff.

[00:03:12] And every single time the data engineer would have to go back and run a sequel command for them on the data and then get that back to them. Mm-hmm. So instead of having them have to be like the, the middle person between that, they built dashboards for their bosses, then allowed them to kind of customize the data they wanted so they didn’t have to like be running these commands over and over and over again.

[00:03:31] Hana: That’s a great use case actually.

[00:03:32] Joe: Well, I agree. And here’s the thing too, like I think realtime data honestly is a very buzzy word today. And I feel like people think they need realtime data. And the problem is most of the time you don’t. Particularly if, if we’re talking about a use case for like an internal dashboard, your boss doesn’t like usually need to know the real time sales data happening at every single moment.

[00:03:52] They only care when they care. Or when they think about it. Mm-hmm. Or if there’s like an alert that goes off that like something needs their attention. But most of the time, most people are just like, I need to, like every week, every Friday, they’re putting together like the sales data for that week or so,

[00:04:06] some sort of report to share to the team or their boss or shareholders or whatever. But it can depend, right? I mean, I think for the case for like a software engineer making a logging system I want to know in real time if one of my servers goes down or a database goes down or we’re seeing a mass Our users are seeing a mass, right?

[00:04:22] Yeah. Implosion of data or whatever. Then I wanna see it in real time. But it really, like, we don’t really care about real time data until we like need it. There is one other use case too. So I worked for a large e-commerce site in North America. They sell lots of appliances and TVs on Black Friday.

[00:04:37] And we did a lot of monitoring, but we only really cared about it one week a year. And that was like the week before and after. Black Friday, we were like constantly glued to our usage. And stuff like that too. But it kind of depends like when you need to like access that data.

[00:04:51] Hana: That’s a good second question to ask once people have identified who their target users is, is to ask yourself, do you really need the realtime data?

[00:04:59] Joe: Yeah. Or like, yeah. How often are they gonna be using it? How up to date? Because I think when we say realtime, we’re still making some concessions about what realtime even means too.

[00:05:07] Mm-hmm. Like, is realtime mean like, To the nanosecond, does it mean the last hour? Does it mean like the last five hours a day? Like how up to date and like how you’re architecting your system can depend on like how actually real time you need it to be. And obviously the more real time you get, the more expensive it gets.

[00:05:23] You’re making some concessions too with like speed and accuracy and all that stuff too.

[00:05:28] Hana: I can imagine when you’re talking to your target users and you ask them like, do you want real-time data? They’re gonna be like, yeah, of course we want the most. Everyone does.

[00:05:34] Joe: Yeah. Right? Yeah. I want a hundred percent uptime.

[00:05:37] I want the data in a nanosecond. I want every data source collected all at the same time. Like, okay, great that gets complicated and expensive fast, but we can figure that out.

[00:05:48] Hana: You do have to ask some questions and kind of read between the lines sometimes when you’re getting the requirements from users, do they really need this?

[00:05:55] And maybe you’ll need to have that conversation with a person that, I know you want real-time data, but yeah, this is gonna be the cost. But before we jump into the challenges, Could you talk more about what are the core components of an effective realtime dashboard?

[00:06:08] Joe: Let’s say you’re somebody who’s data engineer, data scientist who’s like, been asked to put together a realtime dashboard, whether it’s like for customer or internal or just for yourself or whatever, or your team. I think some of the big components you need for that is like being able to get that, first things first, you gotta get your data right?

[00:06:24] Where the data is, is saved, can dictate how quickly, how up to date, and how fast those queries can be. You can do those type of queries and things like Mongo DB or like in Postgresql or whatever, right? But they aren’t specifically designed to handle realtime data, right?

[00:06:41] They’re a transactional database and doing massive data analytics on row based data sources can be like, really expensive. And the, the queries can be really slow and time consuming which can suck. And especially if you’re pulling things on like data warehouses and that sort of thing too. Those are like not designed to be queried at scale in real time.

[00:06:59] They’re just like designed to save your data and run big, heavy, expensive queries. Occasionally. So I think like how and when you’re saving your data, I think is really important for like under, like, firstly, before you even like start thinking about how you’re gonna get that dashboard rearchitecting and re-engineering your data to flow in a way that’s gonna be able to get it in the right place.

[00:07:21] But I think the other piece then, once you get that data in, is making, it’s like use user experience. So like I gave the example of the company making like, Customizable dashboards, so they could do that.

[00:07:34] So they’re querying their data from a streaming data source from an api, and then they’re building a dashboard on top of that. And actually, Hana, I’m curious to hear from your perspective too, like if you have any favorite tools you use for making dashboards or is it mostly like, are you using Excel or are you using like a no-code solution or are you building like custom bespoke reacts front-end components and graphs and D three?

[00:07:57] Hana: I wish I could create those bespoke visualizations using D three. It was one of my goals to learn D three and I need to find time to learn that cuz they look awesome. But for my work, I use Tableau primarily. Mm-hmm. And create, yeah, to create dashboards.

[00:08:10] That’s, that’s what we use in our company and that’s what I’m the most familiar with. Actually, when I was changing roles about 10 years ago, I was. Trying to break into data. I, that’s the first visualization tool that I used and that time was one of the more popular ones. Now there are more out there that I want to learn, but that’s like one of those things I have to like stop myself cause I’m so tempted to try out the latest and greatest tool.

[00:08:31] Joe: That’s the thing too, like, I mean, again, depending on your audience, if I was like on Uber and I was designing the app and I needed to make some realtime alerts for customers, I probably wouldn’t use Tableau. I would make something custom and bespoke. But it, that depends on your audience, right?

[00:08:43] Like right. If your boss is asking for it, it probably doesn’t want you to spend a month developing a dashboard for a simple report he needs by Friday. This is the part that people. I think it’s frequently slept on with using tool sets is like what you’re comfortable with is a huge part of what you should, the tool set, like the tech stack you should be using.

[00:09:01] Joe: If you and your team are most comfortable using SQL and Tableau Cool. Use that. There’s literally nothing wrong with that. The key thing I think is just like making sure that you’re using a stack that you can get things done quickly in and also convey the information that the person asking you for this information can digest it easily and yeah, that’s great. I don’t think there’s anything wrong with that. Between you and me, Hana, I. I’ve used D three several times. It is very it’s a steep learning curve.

[00:09:30] It’s got a steep learning curve. Mm-hmm. And most of the time, like we don’t need to be making, like, I’m not like working from the New York Times making a custom data visualization about some sort of thing. Most of the time it’s like a pie chart or a graph, or like a geospatial data or something like that. We don’t have to reinvent the wheel every time we’re like putting together some report or

[00:09:47] Hana: whatever.

[00:09:48] Joe: That was a long-winded answer, but like core components is the database and where you’re saving the data and then, just like presenting the data in a way that is friendly For your audience? I will put another caveat on that too, for me, depends on your audience of course.

[00:10:01] I love the idea of customizing, like making it easy for people to kind of self-service. Mm-hmm. The more I can remove myself from them getting the information they need from the data, I’m kind of shepherding onto them the better. And I feel like controls, filters, sorting in like a user friendly way can be a really huge way to.

[00:10:21] Communicate that data. Oh

[00:10:22] Hana: yeah. Early in this episode, you mentioned people asking, like software engineers questions about the data. Mm-hmm. And if that can be answered through your dashboard, that’s such a great way of removing the middle person.

[00:10:32] And yeah. That’s actually one of the features I like about Tableau. There’s a ask data feature. Mm-hmm. So even though there’s a dashboard you created, but if the person that’s using it has additional questions about the data itself, yeah, they can ask different questions and get answers.

[00:10:45] So it just removes the need of talking to your data person and asking them a bunch of questions, bugging them from their day-to-day work. So I love that kind of stuff.

[00:10:53] Joe: You know what it, I’m gonna make some bold wild speculations about the future here, but I like. I know something’s coming very soon with like using chat G p T and open AI to query that data.

[00:11:06] For example, even for us as like data engineers in like getting the data in, like it’s important for like, just, we can just describe in plain English, cuz the system already knows the schema. We have the type of data, we have the structure and it, we can just tell it what we want and then have chat G B T and then we pass it, build the schema that we’re using and then have it generate a SQL response for us automatically or, and even like just kind of skip all that, automate all that and just like give us a dashboard as we’re explaining in plain English.

[00:11:33] I feel like the barrier of entry to like. Getting insights from massive data warehouses and all this stuff is like going to like drastically decrease in the near future. Which I think is a win. We’re helping like, put this data together and like the e the better that we can like, help people interact with it, I think the better.

[00:11:52] And I’m not saying we give everybody unfettered access to all the data that we’re kind of in charge of, but like But I think it’s gonna get easier. I, for one, I’m excited. It’s gonna make my job easier. That’s all I’m saying.

[00:12:03] Hana: You brought up a good point because I know people who are listening, there’s some concerns is AI gonna take over my job and or is it gonna get rid of data jobs?

[00:12:11] But the. The kind of task is gonna replace or the tasks that we don’t like doing anyways. Yeah. These are the tasks that take us away from the exciting work. So I’m also excited about it.

[00:12:20] Joe: I know. I love seeing just wildly inaccurate tech predictions, but I don’t think this one’s that far off. No, no. Yeah, this

[00:12:26] Hana: seems reasonable. It’s a,

[00:12:27] Joe: we’ll see. Yeah. I’m excited though. I mean we need data. You need lots of data to train. Models, but like what has more data than a database?

[00:12:35] Like it’s literally defined by the data that’s saving it. And like using databases I think are gonna get very good at sort of utilizing AI models on that data to make, utilizing it easier as developers.

[00:12:48] Hana: I know we talked about some examples earlier on this episode but can you provide some other examples of successful real-time dashboards that you’ve come across?

[00:12:57] Joe: Yeah, yeah, yeah, yeah so I should preface this too. So I work for a company called Tiny Bird. Tiny Bird it’s built on top of Click House, which is an analytical database. But basically what we do is make it super easy to import data. It’s all serverless databases.

[00:13:10] And then we also make it super easy for you to like write SQL, to publish APIs, to utilize that data, which you can be used on. Dashboards. So you have streaming data sources, you can import Snowflake and BigQuery and all these different DA like massive data warehouses and streaming data and realtime data sources, and then easily make a a P I endpoint based on some sequel you wrote.

[00:13:30] A lot of the work we do though is for companies who are doing, like, we’re realtime. Data is super important. So for example, I think the big ones that we’re probably aware of, if you’ve a human being who’s used the internet or a mobile app in the last 10 years, you’ve probably seen some things like in the finance industry for realtime fraud detection, right?

[00:13:48] So if you’re using a credit card and see some weird transactions that are occurring in another country or when you should be asleep, or for larger amounts that you’re known for, like you eat. You need those transactions to go through quickly, otherwise your users are gonna stop using your applica, your banking platform or whatever, right?

[00:14:05] So yeah, your fraud detection has to be massively fast. So it doesn’t slow down because I know this from working at this large e-commerce company that sells appliances that The longer you make people wait to buy something, the less likely that they are to finish that sale. So that’s super important to go super, super quickly.

[00:14:19] So finance obviously super huge. Retail is really huge too for, and I think we’re seeing this more and more and more with personalized recommendations based on whatever. So it’s like your previous history, what you’re engaging with, what other use similar uses are engaging with.

[00:14:33] But when you’re loading up, like I can’t have that site take 10 seconds to load to tell me which. Shirts I should be buying, right. That needs to be near instantaneous or else I’m gone. I think Amazon did a study that was like a, they did ab tests where they purposely sandbag their, their website by like 10 milliseconds and they saw 1% decrease in their revenue.

[00:14:55] Which to them was like billions of dollars. Yeah. So it’s threading the needle with like, giving relevant content but doing it quickly. And that’s where real time data becomes super important. Yeah, transportation. I gave the example of Uber, right? So like we need a notifications in real time when your Uber’s here mm-hmm.

[00:15:11] I wanna see it on that map as it like, Yes, cars around dropping people off. And that’s a real time, right? Like they’re showing the information, they’re sending that geospatial data in real time about to Uber and then they’re sending out to me to visualize on the app. And then there’s other things too.

[00:15:25] Software engineers, logging. If you’re in manufacturing, I wanna see realtime dashboard about like my manufacturing plant or like I got really, I, I bought a house last year and I’ve been really into smart home stuff, so I’ve been really into like realtime logging, energy usage bunch of other analytical stuff on there, but I want like, analytics if something’s weird or notification if something’s weird on there.

[00:15:46] Yeah, there, oh, and then other, like gaming that sort of like like if you’re playing multiplayer games, right? If I’m online, I need to save, I need to transfer data states, game states to multiple online gamers at the same time in real time. Right? I think Google Stadia failed because it wasn’t able to manage realtime data transfer well enough to be a functional product.

[00:16:07] So there’s just a few examples. Like, I mean, realtime is like, I think as a huge part of our online presence right now, and it’s where the industry’s going. And if you’re not considering these things mm-hmm.

[00:16:16] I think you’re gonna be trouble. Like LinkedIn developed a bespoke database just to handle real-time notifications on, on the menu, right? You get that little red circle on LinkedIn when you get a new notification.

[00:16:26] They had to build a new database just to handle that. Oh, wow. I know, right? Yeah. Like, like we’re, it solves a problem that traditional databases haven’t had to solve before, and like we we’re, we’re trying to figure it out.

[00:16:38] Hana: It’s really interesting to hear, and as you’re talking about this, I think we can all think of examples of where real-time data is really needed Yeah.

[00:16:44] In our day-to-day lives. I know we touched upon this as well but it’ll be nice to kind of summarize this. One more time for people who are listening about the challenges with using realtime data and dashboards mm-hmm. And how you recommend addressing them if you do have any recommendations.

[00:16:59] Joe: Yeah. And I’m sure that anyone who’s ever like set up like streaming data, like a Kafka, or Red Panda Stream before is probably like seen that there are numerous issues with handling realtime streaming data. Mm-hmm. But the first one we touched on, which I think is the most important, and the one that we’re probably most cognizant of is data latency.

[00:17:17] Like even if you have a Kafka stream, it’s still using a message queue behind the scenes. So if like things get blocked up, it, the queue just builds and builds and builds, and that can, like, you could still have data latency even though you’re using a data structure or data base that’s designed to stream in data in real time.

[00:17:33] So you have it well optimized and things aren’t getting blocked, we’re still constricted by things like the speed of light which is crazy. This is a fun fact.

[00:17:40] The two slowest things in like application programming are database. Reads and rights and the speed of light. Those are the two biggest issues we solve. And one of those happens to be constricted by Einstein’s theory of relativity that we have the speed of light is constant, right? So we, until we figure out how to like break the speed of light, the only other thing we can do is, and by, and when I say speed of light, like, cause we’re transferring data on fiber optic cables, which the data transfers at the speed of light, right?

[00:18:10] Undersea cables we’re just kind of blinking lights off and on. That’s how we’re transmitting data around the world which is crazy. And that’s why we have like CDNs all around the world so that data gets closer to the user that’s calling them. So the speed of light is less impacted by, or they have less latency by the, I see.

[00:18:24] The speed of light. But that brings us to like data latency with the other thing that’s most time consuming for most applications developers are making. And that’s database calls. So making those are like, making sure those are well optimized. Checking on those two. And I think databases are getting a lot better at handling that too.

[00:18:40] And then I’m sure Hana, you’ve run this one quite a bit too. And that’s data quality. Have you ever run into like bad data as you’re building out dashboards or like trying to visualize some data?

[00:18:49] Hana: The issue that I run into is that when when I do have a dashboard that’s connected to live data, the performance of my dashboards, and usually I’m advised to create an extract of the data and connect that to my dashboard and then refresh those extracts at whatever cadence I choose.

[00:19:05] But it’s not live anymore.

[00:19:07] Joe: If you’re using transactional data or like row based data and you’re having to do a million joins, like that’s really bad for data processing.

[00:19:14] And using that for, and like, and it’s having to do these really expensive operations behind the scenes. So it’s like, maybe even like readdressing, like how that David get gets saved and like how that, or like, Doing some query planning and going in, investigating like what’s happening and why it’s slow.

[00:19:29] Is it doing a full scan? Do we need an index on this? Do we need to put a view on this? Do we need to move this over to a separate database? That’s what you’re encountering, right? With realtime data, depending on the database you’re using, it can really impact performance, which sucks for everybody.

[00:19:41] So, yeah, data latency, huge data quality. This one’s huge too, is security and especially as people were like kind of the gate, like us as being gatekeepers of kind of the data that we’re kind of protecting or whatever. You wanna make sure that like data isn’t leaked. And Hana, I’m sure you and all of our listeners have heard of with database breaches, data leaks, and like all that stuff.

[00:20:01] And like for us, we don’t wanna make sure we’re exposing data incorrectly that users should not be seeing, especially like personally identifiable information, GDPR data. Mm. Hipaa protected data, social security numbers, that sort of thing. Like that could be disastrous if like, we’re not building our systems to be able to protect those data that we’re dealing with.

[00:20:24] Right. Right. So security huge. I had a couple others here. Like, scalability can be big too, but that depends on your database too and kind of how many users there have on there. Usually if you’re building internal dashboards, scalability is less of an issue. But if you’re dealing with Uber or Google or Facebook scale, like scalability becomes a massive issue for real time scalability.

[00:20:44] Cause it gets expensive. Yeah.

[00:20:46] Hana: Yeah, I can

[00:20:47] Joe: imagine. So anyway, those are some of the big ones.

[00:20:50] Hana: Thank you for addressing those. You really want to evaluate if using realtime data is really what you need.

[00:20:56] Yeah. Because there’s all these things you need to take into consideration and it can be a pretty big effort to Yeah. Use it and implement it correctly. So, yeah. That’s all the questions I had for you today. Did you wanna bring up anything else before we wrap up the show?

[00:21:11] Joe: For folks who are like interested in getting started with, with data analytics, I think, and you’re interested in the data piece of it.

[00:21:16] Cause that’s, I, that my side is more like the data and getting it to the Hanas of the world in a way that is going to like, not cause a massive national security breach and get it quickly so like you can get it visualized quickly. Yeah, if you’re interested in doing that, for sure, I would check out check out OAP databases.

[00:21:33] Check out Tiny Bird. We have a free tier, free forever dev tier. In my humble opinion, it’s the easiest, fastest way to get started with analytical realtime database. And we have an API builder on there automatically. So you can just like, add some data and then make an API endpoint, start using it on wherever you need it.

[00:21:48] Super easy. So anyway, if anyone wants to do that, you should for sure check those out. And then otherwise, like, just like try it, try it out. I don’t know, like d i and I’m sure like the hard part’s, getting data and then like, kind of getting into it and it’s kind of abstract. Data’s not very visual on like the front end.

[00:22:03] I, I like the idea of like using no code tools to try to just like get something that’s usable quickly. Mm-hmm. Because I learned by doing, and I recommend folks who are like, like me or wanna just like jump in, try a thing out and try visualizing some stuff. Even if it’s just like your Google Analytics data or I don’t know, like some sales data from h and m or something you find online.

[00:22:24] Like just kind of play around with that. I think it’s the best way to like get experience and for me. Running into bugs is the part where I really learn the best. Mm-hmm. I can start Googling from there. So anyway, it’s a weird, hard place to get in actually. I’m curious, what, how would you recommend people get into the data side of this?

[00:22:39] The space?

[00:22:40] Hana: Your recommendations are exactly What I would also suggest is start to actually work with data the issue though, with, Data professionals or, and people who are aspiring to become data professionals. Mm-hmm. You get very tempted to keep taking courses or learning a new skill, but you learn the best when you actually do.

[00:22:55] Even if you can think of any data around you in your personal life that you can use. Yeah. You can also on my website, I do have a list of places where you can get. Data for free and start working with that kaggle’s one side. But yeah, it’s really easy to get started with that and play around with it and when you run into limitations, I also agree.

[00:23:12] I’ve learned that from experience as well. Mm-hmm. When you have limitations and constraints, that’s where you have opportunities for creativity and like problem solving. It’s really exciting. So, yeah. Hundred percent agree with that.

[00:23:23] Joe: Shout it to Keel. I love it. I, they have amazing data in there, like massive data sources, which would be really fun to play with and like, and for us, like it’s helpful for like scaling and seeing if it can handle these massive data loads their production like similar site.

[00:23:35] And then the other thing practicing I, because I get the same thing, tons of people are asking me like, what are good, like videos to watch, which is like, cool. I, it’s a good place to. Get introduced to it, but like Michael Jordan didn’t get good at basketball by watching basketball videos like he was out practicing.

[00:23:49] Right? Like, you get gotta get on the court and get messy and sweaty and bump into some skin, your knee it’s, it’s, you’re gonna have to get out and practice too. And I, I, I would push you to get out before you feel fully comfortable to go try it out. Mm-hmm. So yeah. Good start.

[00:24:03] Start with videos, but. I’m gonna push you out the door a little bit and encourage you to go and build some stuff. Otherwise, that’s it. Follow me on TikTok, follow me on Twitter. Check out Tiny Bird. And I

[00:24:14] Hana: think that’s it. Awesome. Yeah. Can you tell folks again where they can find you on Twitter and

[00:24:18] Joe: TikTok?

[00:24:19] Yeah, you can find me at Joe Karlsson, the number one on TikTok. I have my website, Joe Karlsson, k a r l double s o n. And yeah, TikTok, just my first and last name. and people can connect with me on LinkedIn too if they want to or ask questions. Dms are open.

[00:24:33] Hana: Perfect. I’ll be sharing the links to these as well as the website for Tiny Bird in the show notes for those who are interested in checking that out.

[00:24:40] Thank you again, Joe, so much for coming on the show today to share all this helpful advice about building interactive dashboards with realtime data. This was really insightful for me and I’m sure for a lot of listeners. So yeah, thank you for your time.

[00:24:51] Joe: Oh, thanks for having me. This has been a blast.