​How to Prep Mapping Data for Tableau (Become a Geographic Viz Wiz!)


Description

Learn how you can easily blend spreadsheets and other data sources with complex geospatial data and output directly to Tableau. You’ll see how to spatially enable your data and pick up FME tips on data cleansing and preparation for Tableau. Plus, via examples from Tableau users – including a live demo by Athena Intelligence – you’ll learn some of the advantages you can gain by actively using location data in Tableau.

Transcript

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Don: [00:00:01] Good morning. I'm here with Laura Kerssens, from Safe.

Laura: [00:00:04] Hello.

Don: [00:00:05] She's our Tableau expert, I guess we could say. She's the one who's played around with this the most and you'll see some for her work via web interface later today. My name's Don Murray and I'm one of the founders of Safe. And we're on the line with David from Athena, are you there David?

David: [00:00:24] Yes, I am. Good morning.

Don: [00:00:26] Good morning. So anybody else there at Athena with you?

David: [00:00:31] The team is actually, we're on a couple of projects so it's just me here today.

Don: [00:00:36] Okay. So with that we'll get going. And this morning we're gonna talk about how to become a geographic viz whiz. And that's one of the new systems we've added support for with our tool now, is the ability to make it really easy to move data into Tableau. So we're gonna show you all about that this morning.

[00:00:55] Okay, so look a little bit about Safe and our FME technology. We work with all kinds of data but we grew up around spatial data. So we started with CAD and GIS and then spatial databases and spreadsheets. And on this slide now you can see all the different types of spatial data that people are working with and that we support. So there's everything from BIM models, which we have folks at the University of Massachusetts Amherst have used BIM to bring it into Tableau to look at campuses and how to use their space efficiently and things like that.

[00:01:37] There's also, you know, you have web data so it could be web services. You wanna connect to web services and bring in data from web services. Sensors, there's lots of sensors out there and again we can bring data from sensors into your Tableau. So anything that has a spatial component to it we can bring it in. So we're pretty excited about that.

[00:02:01] A couple of other users of our stuff into Tableau is our friends at, I'm just looking for my note here. At NOAA they're using Tableau with bathymetry data to bring that in. So that's pretty exciting. And basically they collect a lot of oceanic data with bathymetry data and they bring that into the Tableau system.

[00:02:28] And another one is our friends at the European Environment Agency. And they have data from enterprise application. They use FME to send data to Tableau for reporting. And it's all around RAS or images and vector tiles with the data warehouse and mapping all the data into spatially located cubes so that people can easily click and find out data about all that. And again they're using Tableau.

[00:03:00] There's also on our blog this morning, there's a good article about Tableau and FME. And Roger will Tweet out blog.safe.com around our friends at INSER showing us how they have used FME spatial data in Tableau. So we're pretty excited about the capabilities of Tableau and the things it can do with data when you get it in there, so yeah.

[00:03:31] Okay, so this is the work that Laura did that she'll show it to us. And basically what we provide is a free shape file to Tableau online service. So basically you can take your shape files, drag them onto the web page and click "convert" and you get a TDE file. So it's just as simple as that.

Laura: [00:03:55] Yeah, pretty much. So it will bring all your geometries across. You can see the geometries represented in Tableau's data model. So I think David's gonna be showing us a little bit of this coming up so you get to see it in action, coming up soon. But we do have a link down here, the shortened one, the fme.ly/SHPtoTDE so you can try it out for yourself. If you've got some shapefiles lying around you can give it a whirl and see what that brings for you.

Don: [00:04:19] Yeah, and again this is just sitting on top of, this is a nice interface on top of our FME workbench technology so it's really just the basics. And then David will also show us later how you can take that and do more sophisticated stuff.

Laura: [00:04:34] Yeah, that's right. So we're gonna get into a little bit here. So I'm just gonna show a quick demo of FME in action just to give you an idea of what it looks like when you're taking your data from any input source, any input system and bringing it over to Tableau. So I'll just show you what FME looks like right here.

[00:04:58] So what I have opened here is our application called FME Workbench. It's essentially where you decide how you're taking your input data and how you want to bring that into your output locations. So on this case here we're taking a CSV file and we're converting it into a Tableau, TDE, the data extract format. So FME Workbench itself is a graphical user interface so it's basically a drag and drop interface where you bring in your source data and then you can pull in what we call transformers to work with the data itself.

[00:05:36] So in this example here I've got my CSV file represented in our Workbench canvas. So I can see all of the column names and the CSV as they've been imported.

Don: [00:05:46] Right. So that's really just like of a view of a table.

Laura: [00:05:50] Yeah, exactly.

Don: [00:05:50] Yeah, yeah. So if I was using a database I'd see a database table there with all these attributes?

Laura: [00:05:55] Yeah. So I can go in here and I can see all the attributes and their attribute types and all that. So you get a really good idea of what kind of data you're working with to start. And yeah, and this could be anything. So in this example it's a CSV but it could be a database. It could be a CAD file, it could be a model, whatever it is you have.

[00:06:12] So from here what we do is, what FME will do is it will read in that data set and it'll bring it in one record at a time so you can work with each individual record in your dataset. So you can then work with that. You can look at the geometries inside. So in my example here I'm reading in a CSV file with latitude and longitude. So FME is converting those into points and then I'm filtering it out just to make sure I only get the points out of that for anything that maybe had an invalid latitude or longitude. I'm gonna filter those out and take a look at those later if my data is incomplete in places.

[00:06:47] We have this transformer here called an attribute validator, which basically lets me take a look at all of the column data inside my CSV file and just make sure it passes my validation rules.

Don: [00:06:59] Okay.

Laura: [00:06:59] So in this example I've got number of employees and I wanna make sure that's only in numeric type and if it's not then I want to filter those out and fix those up. So it's a good way to clean up any potential errors in your data.

Don: [00:07:11] Right. So you can also use FME for QA, QC as well?

Laura: [00:07:14] Yeah, exactly. And then there's lots of other tools within FME as well for dealing with geometries. If you've got points you wanna convert those into lines or convert those lines into polygons and whatever else. So you have something like our attribute manager here, which is dealing with the data values themselves. So I can come in and decide how I want those columns to map into my Tableau extract so I can rename those columns. I can remove columns that maybe I don't want in my output.

Don: [00:07:45] Right. And I could process two separate data sources and I can even rename attributes so they all lined up?

Laura: [00:07:51] Yeah, exactly. Converge them all together into a single file. So there's a lot of power in here and you have a lot of flexibility in what you can do within FME.

Don: [00:08:00] Good.

Laura: [00:08:01] Yeah, so that's just basic example here. So I could run this and get my TDE extract from my CSV file, which I've already done. I can see it represented. This is the data itself in Tableau. And then I can see the representation of the point data that I pulled out directly from that CSV.

Don: [00:08:19] Okay. Could you go back to that workspace just quickly?

Laura: [00:08:22] Yeah, for sure.

Don: [00:08:22] And just bring up the reader dialogue, just to quickly show all the different, the hundreds and hundreds of formats that FME supports?

Laura: [00:08:33] Yes. [inaudible 00:08:33]

Don: [00:08:35] Yeah, so, yeah. And these are the ones that are kind of formats so that would be databases and file based. And then for web services then you connect to those often in a completely different way. But suffice it to say there's many hundreds of different data sources and places, yeah.

Laura: [00:08:56] Yeah, so there's a lot of different things you can bring in and restructure.

Don: [00:08:58] Absolutely.

Laura: [00:08:58] And take those into Tableau.

Don: [00:08:59] Yeah. Good.

Laura: [00:09:06] Okay.

Don: [00:09:07] So that's great. So with that we're gonna pass the baton over to David of Athena. And David at Athena and his team there have been really early adopters and really partners and bringing our Tableau capabilities to FME. And he's a power user of Tableau so we're really excited to see what he's done and to show you today. So do you wanna take it away, David?

David: [00:09:40] Sure, thank you. Let me pull over to that and make sure I've got all the buttons clicked properly. Can everyone... I assume the presentation is coming through okay?

Don: [00:09:51] Yeah.

David: [00:09:53] All right. So thank you guys for inviting me to join in this. I can't say enough how powerful the connection between FME and Tableau really is, you know, it's like peanut butter and chocolate. They are two great things but together it's even a better thing. So Athena Intelligence basically we provide business intelligence to the food and energy systems by pulling together what we call the data of land, food, water and energy. And that is, as we've been talking about in this presentation, really just a massive collection of everything from ground water basins to well data, weather data, satellite imagery, private data like contract numbers and names and field locations and irrigation applied. Hundreds and hundreds of datasets that are involved with the production, processing, manufacturing and distribution of food and its use with natural resources.

[00:10:58] So while it sounds like a mouthful, you know, basically what we wanna do is be able to provide the stakeholders like Campbell Soup, for example, our customer, one of our primary customers, the ability for them to engage their supply chain, collaborate them in ways that allow optimum use of resources or water, minimize the risk of resource constraints and still maintain a steady and consistent sustainable supply chain.

[00:11:30] So doing that obviously requires the aggregation of lots of data sets that have never been pulled together before. And they are in all sorts of different kinds of formats and structures, everything from shapefiles to Excel sheets to PDF documents to handwritten notes. And the problem really is around getting this data in a position to be blended and visualized. You know, often people talk about the problem of water and the problem of sustainability and the problem of energy. What Athena sees is that the elephant in the room is that it's a data problem. And the problem isn't that there's not enough data. There's plenty of data out there.

[00:12:18] The problem is that the data can't be pulled together to be used to drive any kind of intelligent decision making. And so it's been a well over a year and a half effort on pulling all of this data together. And I am not ashamed to say when I came across FME part of that was I was thinking that we had bitten off way more than we could chew. But FME came to the rescue for that and you know, when we started utilizing and becoming more comfortable with it, I did get a little emotional because of the time and the ability to do the things that we needed to do to get the data processed.

[00:13:04] And then of course coming across Tableau, that gave us the tool to be able to now take the data and visualize it and provide it to our customers in a way that they could digest the news. So now what was once a bunch of data that really couldn't be used to gather kind of a, just a hodgepodge of data and this kind of just garbage is now business intelligence through our application to where again, Campbell Soup can begin to pull in hundreds of datasets, create profiles out of those datasets and then begin to assess and collaborate with their supply chain in various terms of risk or opportunity.

[00:13:49] They're able to see year-over-year improvement or change to management practices and their impact on regional ecosystem impact or yield impact, able to do various different analytics and reporting out to the supply chain or to the market in general.

[00:14:08] So we have these available if you click on the link here and play with some of these visualizations. But as I like to say, the visualizations are, they are nice and naturally people are drawn to the pretty colors and the graphics but visualizations aren't very useful unless you get the data prepped. And so what I thought we'd do is just as I mentioned earlier, just show a very basic example.

[00:14:38] And what I've done is I've loaded up the ground water basins of the state of California. And from that pulled it into FME, as you see, here is the table. And then a quick demonstration about how rapidly to pull together the data and create something that has some sort of intelligence. And so I know this video is being recorded so I won't go into the details of the exact process but we'll see how rapidly FME allows you to put together a dataset and then through Tableau, through simple tools within Tableau, draw some sort of intelligence or information about it.

[00:15:36] And create a parameter, say basin context. And then you wanna be able to go, I'm gonna change the colors so you know which basin is which. I know this seems menial but it's actually, it would take half a day to be able to do this without the combination of the FME or Tableau together. So Tableau allows you to run or create these calculations when...excuse me.

Don: [00:16:43] And typing speed always drops proportionally with the number of people watching?

David: [00:16:48] Isn't that amazing?

Don: [00:16:49] Yeah.

David: [00:16:51] It's amazing. Sub basin. This will be worth it. Calculations valid, parameter control. Voila! And now you can begin to filter the entire dataset out and [inaudible 00:17:45] to do any analytics. That was just a simple calculation around being able to visualize the basins and which basin exists where. But of course if you can add in, I've done other things recently with ground water wells and so you can begin to see the average depth of the ground water wells or filter out a groundwater well or well count or average depth per groundwater basin, etc.

[00:18:15] And so while, you know, these are very simple examples, really the power of FME and Tableau, especially FME, is able to take the data and run... here is just a brief example of a very simple one. But what we've done at Athena is that we've got dozens and dozens and dozens of workflow such as this where you're just pulling in the ground water wells, weather data, soil type and the ground water basins to be able to produce analytics such as this, which includes four or five dashboards simultaneously connected to where you could change the context on the fly and being able to see how much irrigation is used via ground water surface or a combination of both sources by county or by groundwater basin, your acreage in the context of these groundwater basins and risk of ground water depth, or how that relates to the entire central valley. Filter out the data to where you could begin to see exactly which fields lie within which risk areas.

[00:19:40] And this is beginning to drive collaboration between not just Campbell Soup and its suppliers but all of the regional stakeholders within a particular ecosystem, such as NGOs and conservation groups and energy suppliers. So it's because of FME that we can pull together, you know, half a dozen different data types, several dozen different data sources simultaneously and make it easily blended to drive contextual collaboration amongst multiple stakeholders.

[00:20:14] So that really is the power of the relationship between FME and Tableau, is that Tableau is a wonderful tool, very powerful tool. You can do lots of pretty interesting things with Tableau. But it's only as good as the data that you put in it. And FME truly is the magic workflow that brings Tableau to life and ultimately companies like Athena Intelligence can now drive value to its customers. And ultimately as Athena we would like to think that through that value we can drive a better planet.

[00:20:54] So that, in a nutshell, is Athena Intelligence and how we've used both tools of FME and Tableau.

Don: [00:21:08] That's impressive, David. I'm really impressed with how you tied all the data together so when you click on something in one window in Tableau all the other windows of your dashboard update. Us being new to Tableau we're pretty impressed with some of the things that it can do as well, so that's fantastic.

David: [00:21:31] And I have to say there's... Athena has made quite a name for itself within Tableau because we're doing things with Tableau that they are, I would say, impressed with. And the reason being is because of FME that we're able to get data prepared in such a way that we can leverage tools or do things within Tableau that are unique in nature.

[00:22:03] You know, ultimately what Athena is about is being able to take the data and blend it and be able to show a temporal, spatial and dimensional context to data in any way, shape or form that the user wishes. And being able to do that without going through a lot of effort on 20 different dashboards, being able to make it all within a single click away for someone's use.

[00:22:32] So the ability to do these real time analytics, this is something that's very powerful. And now we're moving to the next phase of our offering our product. And I know you guys are gonna talk about this but that's the automation portion of this. And so what we are now doing is we're working with you guys, as you know, but for everyone on the presentation that we're... this is on the server, on FME Server and through our platform, online platform to where we are telling FME to go pull data sources automatically. For example, in cubic feet per second the river flow of every single river, stream and lake in the entire state of California.

[00:23:19] So we know exactly how much water is flowing through every single river stream and lake in California, where it's going to, how fast it's moving and can overlay that with, or integrate it with hundreds of other datasets such as irrigation use or demand or energy or land use etc. All of that is coming in real time and is able to be processed, put together by FME then pushed out through visualizations and data services.

[00:23:50] So it's really an incredible tool, powerful tool. And for those stakeholders that really need to drive intelligent decision making, they don't have to spend 90% of their time now fumbling around and trying to put the data together. There are tools that can dramatically cut that time and get people to actionable execution much faster.

Don: [00:24:19] Yeah.

David: [inaudible 00:24:20]

Don: [00:24:21] Yeah, wow. That's fantastic. That's fantastic. And I think that brings us into the next step where we talk a little bit about automation, because one of those, the great things of FME of course is it's all about automation. You build these workflows and you offer these workflows once and then you just rerun them every time you get new data. And of course with the desktop product you can do that from the workbench, run it every time you get new data. You can also write a script that might run periodically in some sort of a batch environment.

But there's lots of other ways about automation and FME come together. When it comes to we have a product called FME Server. And really FME Server is all about bringing the power of FME automation to the enterprise. The FME Desktop, Laura showed briefly the graphical interface. While it doesn't require you to write any code, it is a technical tool and so typically within an organization there's a handful or 10 or a number like that that are really the strong FME users. And with FME Server, if you can bring those workflows and make them available across an organization so that anybody who has access to this data or wants to produce a TDE file can just do that and they won't even know that FME is there.

[00:25:49] And one good example was that initial web page that Laura has built. That shape to TDE. People go there, they drop their shapefiles down and after dropping the shapefiles down and clicking the 'Submit' button causes the FME Server in the background to do the work. And again, the user doesn't have any idea that FME is anywhere. And that's really the whole point, yeah.

[00:26:13] So I think we'll do the slide. That slide here. And while she's doing that I'll talk a little bit about the different ways that that automation can happen with FME. FME Server has a number of these things we call watcher so there's a file watcher, directory watcher, in which case if a file appears in a directory anywhere on your network that you're, in any directory that you're watching it will trigger FME to do work.

[00:26:40] We also have an FTP watcher. So again you could have people uploading data to an FTP site and whenever data arrives in an FTP site, FME will notice that and then trigger work. We also have a Dropbox watcher, which I mean if a file appears in Dropbox. And through our web service technology you can basically also do it for Google Drive and Microsoft OneDrive and all these different web-based places to put data as free. So Amazon has three, again that's another one.

[00:27:12] And of course email, you can have FME watch email. And if an email arrives then we can take the attachments out, process the data and again use that to drive automation. So there's schedules. And I'm sure I've missed them. I missed a few, but you get the point. Really we want you to be able to set up something so that you no longer have to check when new data arrives. It can just automatically do it for you by either having a schedule or having some event trigger the execution of an FME job. Yeah, so hopefully I think, anything I missed Laura that comes to mind?

Laura: [00:27:57] No, I think you covered that well, yeah.

Don: [00:27:58] Oh yeah, okay. Okay. So yeah, so I think for this webinar we tried to keep it really high level just trying to show some of the value that Tableau and FME bring to the table. For a long time, FME users who are here, this may be the first time you've seen Tableau and it's a very impressive tool. And we're excited by some of the things that we're seeing people do at Tableau.

[00:28:32] For Tableau users we recognize that you're in the same boat as everybody else and that is you're probably drowning in data. The world today, it's not, "Dude, can I find the data?" it's more, "How do I get the data in a format that's usable?" And that's really what FME is all about. Bringing the data wherever it is to wherever you need it, and in this case it would be Tableau.

[00:29:02] And David has just been fantastic. He knows FME and he knows Tableau and so he was able to show us some of the things that he was able to do with those two tools that without those two tools it would have, you know, might not have been possible or the cost of trying to build it would have been orders of magnitude to faster.

[00:29:23] So, yeah. So together if you do start bringing spatial data into Tableau and get the whole map, we all love maps, and being able to see the data spatially where it is and be able to, you know, intelligently drill down and see data, it really enables you to take your decision-making to the next level, yeah.

David: [00:29:45] I wanna echo that point there that you made that the business case for this is that it has magnitude of, it would cost a lot more. It would be potentially unfeasible for a company to be able to do what Athena is doing in a startup. I mean, presently, I'm sure we're all familiar with this, for the data that Athena is pulling together right now it presently takes large government grants and teams of grad students and months of work just to be able to pull the data together. And 95% of their time is just trying to get it and kick it into place to where it could be used for something.

[00:30:29] And so the combination of FME and Tableau have made it commercially viable or commercially possible for companies like Athena which, you know, we have deep insights and intelligence and relationships within the food, water, energy nexus. But all of that intelligence and experience and relationships, it's impossible for us to commercialize that unless we had these tools at our disposal. And now we're able to, it basically unlocks the value and the potential of data that exists and marries it with industry expertise to drive something that is commercially viable and usable in the marketplace.

[00:31:13] And, you know, really what makes it commercially usable and viable is, as you mentioned spatial data is wonderful, tabular data is wonderful, but when it's combined together it creates a whole new different context. And you know, I've seen over and over again lots of different growers or production managers of large supply chains that once you just show them the spatial and tabular analytics together in the same dashboard, that the wisdom that is unlocked through those people is exponential.

[00:31:47] And so there's a lot of untapped value that the combination of FME and Tableau are able to open up. I just want to really hammer that, make sure that horse is whipped dead, that we got that point across. It's not just a pretty, cool, fun tool. It opens up previously unrealized commercial and other avenues of value.

Don: [00:32:15] Yeah, thanks for that. Yeah, and another thing is once you've... the users who typically use FME are definitely not coders so you don't have to be a coder. Now some are, of course, because we have interfaces to things like Python and TCL and R and you know, Java and things like that but 90% of our users at least are not coders. And with FME, with that big graphical workbench you can easily just go drag and drop things together.

[00:32:45] And the other thing is if you've built a workflow and then all of a sudden you discover or find a new dataset that it's really that has useful information it's really easy to bring it in. So yeah, so those are just some of the points there. So yeah, so I guess it's time to... Anything else, Laura?

Laura: [00:33:04] No.

Don: [00:33:05] That's it?

Laura: [00:33:05] Yeah.

Don: [00:33:06] So we can look at some questions now. So there's been lots of great questions so thanks for those. One of the ones that was asked fairly early was, "Is there a file limit size to data that FME can work with?" And the answer to that is no. We work with datasets that are gigabytes and when it comes to point clouds, you know, terabytes and size. We also connect to databases and we have workflows that have read billions of records. And we continue to work and then... I can't say too much but in the next release we're doing some overhauls on a few things to make our processing in order of magnitude faster.

[00:33:48] And speed is something that people already think, when at FME they think that's one of its assets. So if there is any limit to file size that would be on the TDE file size. I'm not sure if there's a limit there or not but that would be, if there was a limit anywhere, that's where it would be.

[00:34:11] Somebody else asked, "Can I read more than one dataset at a time?" Absolutely. The beauty of FME is you can add as many readers as you want and you could do the data merging right within the workflow and there's no limits to the number of readers or transformers or things like that, so.

Laura: [00:34:29] Yeah, and also to the number of files a single reader could bring in.

Don: [00:34:32] That's right.

Laura: [00:34:33] You've got a directory in CSV files that you wanna process...

Don: [00:34:35] That's a good point.

Laura: [00:34:36] You deal with them all at once.

Don: [00:34:37] Yeah, yeah, yeah. There is another one that there's an old slideshare that showed FME going to Tableau. And I just wanna point out that that slideshare is done before we added the TDE writer and so it showed how you could use FME in a more bare bones manual way where we actually wrote catch file that then you could load in to Tableau. So it was slower but it was also when get it into Tableau it was more work. Now we write the native Tableau TDE file so that slideshare is now really a historical document and not one that is relevant.

[00:35:29] So let's see. Some people asked about polygons. Yeah, so our TDE writer does points lines and polygons and the TDE writer looks after building all those for you. So you don't, when you load it in the polygons are built. And it was demonstrated a little bit there today, so yeah.

Dale: [00:35:54] So Don? Hey, this is Dale here.

Don: [00:35:57] Hey, Dale.

Dale: [00:35:57] Some folks have said that Tableau itself has some limitations as to the number of vertices that it can handle with geometry. And I'm wondering Don, does FME have tools for maybe taking some vertices out?

Don: [00:36:11] Yes, it does. So great question. Yeah, so Laura can even bring it up. You can count vertices but there's also generalization tools. And what generalization tools do is they offer a number of ways of dating out the data. And they try to do it intelligently so if you have a lot of points along a line it will pick them out. You can specify tolerances to maintain the original shape as best you can or at [inaudible 00:36:41] that went down, Laura. And you can see there is... and just click on the red thing just to see or ended up the algorithm. You can see there is just tons of different algorithms on Douglas.

[00:36:50] So basically, you know, if you have a favorite way of generalizing your data to reduce the number of points, then knock yourself out. Yeah.

Dale: [00:37:02] Don, I think also some folks said that sometimes there's polygons that are just too darn big. And I think we have a trick up our sleeves for polygons as well, that you can chop them into little pieces, right?

Don: [00:37:13] Yes, that's right. So if Laura types, I think she types "chop" so Dale gave us the clue there. Yeah, the chopper, it will... Is that the one you're thinking of?

Dale: [00:37:22] That's one of my favorites.

Don: [00:37:24] Yeah, yeah, yeah, yeah. That's an oldie but a goodie. And then, what you can end up with is all the smaller polygons they will fit together, sort of like a square jigsaw puzzle. So this is in my favorite kind because it's really easy to put in the piece anywhere. And also then you can maintain the complexity of your outer boundary if you need to and yet still maintain the number of...

Dale: [00:37:49] And in fact, Don, you know, if someone had lines that they wanted to preserve every vertices on that river or creek, they could go onto the chopper and then they would just make a bunch of little lines that Tableau would be happy to display but not none would be too many vertices.

Don: [00:38:03] Absolutely, absolutely. They could do that too. Yeah. So those are great questions and great points, Dale. So you know, basically what you need to do with your data, we've been doing this for 20 years now or probably a little bit longer and so chances are that somebody else has come up with those problems and so we have a way. And if you don't, can you bring up our website quickly? And so if you have any questions using FME, so that's the one thing, a few people ask "How do I explore this tool?" Well, we have free trials, just knock yourselves out. We give you a 30-day, 60-days whatever you need until you determine if the product adds value to you.

[00:38:44] So we don't wanna sell you a product if it never makes it to production because it doesn't solve your problems but we're pretty confident that it will obviously or we wouldn't have that kind of a program. And also where it says, "Leave a message," down there when you come on, if Laura clicks that, a lot of that, you can leave a message or a question. Most of the time or probably around 9 o'clock today you'll see that we're on there for live chat. So while we're really proud of our product and we worked hard to make it better, we believe the restaurant model where the service is really the key to everything.

[00:39:20] And we have an amazing team of experts at Safe who really get a lot of kick out of helping people, who love FME and they love working with our users and learning new stuff because they learn new stuff too. So yeah, so reach out to us or support@safe.com if you don't like either of these approaches. And yeah, Laura is one of our experts at Safe so, good.

Dale: [00:39:48] Don, folks are asking as well if FME can scrape data off of websites or hit rest APIs.

Don: [00:39:56] Absolutely.

Dale: [00:39:56] How are we doing that?

Don: [00:39:57] Absolutely. So yeah, so rest APIs we have, one of the new things we got at the FME is the ability also to connect to any web service that supports OAuth too. And so any web service out there you can scrape it or you can talk to the rest API websites. HTML is just a web page. You enter the URL, the HTML comes back and then we have tools to help you parse the HTML and extract tables or anything else that might be, you know, that might be on that website. So yeah.

[00:40:32] And again, you know, reach out, use our great team of experts here. And we can send these examples, samples because all of us, there's one thing we all like when you're trying something new and that's an example or a sample. We all hate the dreaded, you know, blank page whether they're writing code or using FME and starting from scratch. So yeah. Okay, anything else?

Dale: [00:40:58] Scraping off PDF, someone is asking.

Don: [00:41:01] We would love to, that's on our to-do list [inaudible 00:41:05].

Dale: [00:41:09] Yeah. The number of people are asking about, you know, name your favorite format? Can we go from that to TDE? So file due database as your STE, Oracle spatial.

Don: [00:41:20] And there is a good link that I'll post that, because a few people have asked that so I can post it into the group here. I'll just put it in the chat right now. All our formats are here. There it is. So I just chatted that out to the group. And then you can see many of the web services, of course with the web services there's hundreds out there. We have the logos of just some of the top, more popular ones. And then there is a great format search page there where you can look at the hundreds of different formats that are listed there. So yeah. Anything else?

Dale: [00:42:06] Someone talked about the use case. Interestingly enough, sometimes there is like some users don't want to actually go to TDE files because they can use Tableau to connect to a database. And so they're using FME to go from some non-supported spatial database and then using FME tools to basically split out and create the vertices records and so on and go to SQL Server and then use Tableau, the directed read from SQL Server and visualize.

[00:42:34] And of course that workflow is possible. It's more complicated, but you can do that. We would recommend folks try to use TDE instead because that's just simpler. But if you really want to, for data volume reasons you could of course use sequel server as a staging place.

Don: [00:42:49] Yeah, yeah, yeah. And yeah, that's exactly right. The only thing I'd add to that is if people could send us an example of their Tableau and SQL Server, we would be able to make that easier for them. So then they might be able to say Tableau, SQL Server and then we would look after making sure that the column names are right and all that sort of stuff. We could do that. So yeah.

Dale: [00:43:15] Hey, you've got a question here that maybe David might chime in on. Someone asks, "Can David explain more about the wisdom gained by growers and how does access to all this data and visualizing it change their behavior?"

David: [00:43:30] Sure, yeah. Well, for example they're able to, some of this is somewhat confidential. But what we're able to understand is that there are certain management practices that are associated with the delivery of a quality level of the crop, which has impact to how the growers are paid. Campbell Soup sits on all of their growers and Campbell Soup is using Athena to collaborate with their growers on capturing how the growers input their water and management practices of nutrient etc. They're able to report back down to the growers on an anonymous basis where the growers stand in context of other growers and what management practices across their entire supply chain are producing certain results.

[00:44:32] And so when the growers are seeing that, they are beginning to then see the data driven intelligence to push them over the edge, if they're sitting on the fence that they should use or if they should modify their management practices etc. We've seen already, I mean, we've only done this for a year now with Campbell Soup and we've already seen a 10% shift in a specific management practice through the supply chain just simply because of the data.

[00:45:04] And Campbell Soup has been working about three or four years to try to move their supply chain in one direction and it was simply through Athena that they were able to report back down to the supply chain and saw an immediate bump in that direction. So just being able to see the data and visualize it and then see how they compare to other management practices has been able to provide the convincing arguments for a shift.

Don: [00:45:45] That's great. So yeah, there's another question here. Somebody who is a student and said,"Do you have any student or academic licensing discounts?" And absolutely we do. The cost for a student or academics is free. We just give it away to students. So if you're a student or at any institute, school, let us know and we'd be happy to provide as many FME licenses to you and to all your other students and to the education institute for free.

[00:46:22] And really, you know, anybody out there who wants to learn FME, if you're not sure this tool is what you, just grab the bell, and as I said we're very generous with that. You can do anything you want with it. And then buy it when you've actually put it into production, is really kind of the way we work. Yeah, so.

[00:46:39] Okay, so with that, I think next slide. Okay, so yeah. So again, this is the, there has been a few people ask about that link so try this. It's just a straight generic shape for TDE. The TDE you get out is exactly equivalent to the shape you put in so there's no magic. But underneath it is a very simple workspace that Laura has put together. So yeah, so give that a try and use it with reckless abandon, I guess. Yeah. Anything else?

[00:47:15] Yeah, okay, so some resources. So the intro video you can see there that's about two minutes that talks about the value we bring to Tableau. There's a blog article that just went out today from a partner of ours who has used, INSER from Switzerland that's talking about how they use FME to bring spatial data into Tableau. Lots of knowledge-based articles.

[00:47:41] There's an FME Hub, you're gonna see some custom transformers in there from our good friend, Jan Bliki at the European Environment Agency. So we're gonna be hitting him up for a sample workspace because we've been pouring over these and we're pretty interested in seeing what he's done there. They look really great.

[00:48:01] And I should mention that we are going to be in Austin, Texas at the Tableau conference on November 7th to 11th. I'll be there. Laura will be there and Roger, who is in the back room, who we keep in the back room, he'll be there. And so we're looking forward to that. We're gonna be hanging out with Bill Nye, the Science Guy and out Shankar who does the "Hidden Brain" podcast. So we're looking forward to that. We have a booth there so if anybody's going we would love, we always love it when people come by and talk about anything data related and data challenges and things like that. So that's gonna be here before we know it and so we're pretty excited about that as well.

Laura: [00:48:45] Yeah, definitely.

Don: [00:48:46] Yeah. And with that, that's it. Yeah, so thank you, Laura. Awesome, Laura did all the heavy lifting on this with Roger and Stephanie. And I'm just here for my good looks, really. Laura laughs. So anyway, and thanks Dale and thanks for everybody else at Safe that answered the questions.

[00:49:08] And a special thanks to David at Athena. You really made this webinar valuable to people, I think. Really showing what you can do with FME and the power of Tableau with spatial data. So we're pretty excited about that. And thanks so much David.

David: [00:49:30] Happy to. And I want to thank you guys for the support you guys have provided. You know, all the data exists for each of us to do what we feel we need to do. It just takes tools like you guys to allow value to be created. So thank you guys, very much.

Don: [00:49:50] Yeah, awesome. Awesome. And yeah, and if you wanna give FME, learn a bit more about it, go to www.safe.com and download it and ask us questions. We'll help you get going. And really, look forward to working with you and learning from you and sharing what we can do to hopefully help you guys, everyone be more productive and have more fun with data. Good. So thank you. And we actually finished early. This might be a record for me. Yeah. So excellent. So thank you so much.