Ingest Salesforce & SAP to Databricks in 15 minutes

0:00I love the fact that we focus on one thing make data movement as simple as essentially turning on your lights ingesting some data looks super easy AI ml gen AI all right folks uh so today we have with ky ky is working for five Tran uh and F Tran is specialized in moving the data around I know a lot of customers are using F Tran at their bricks um kayy why don't you talk a

0:24little bit more about you and and what you do at Fan yeah absolutely thank you Quinton uh it's great to here by the way really appreciate you having me on the show um yeah so I've got partner sales engineering I've got a small Global team that is working with our top technology Partners like Databricks plus Consulting Partners around the world helping to really put together really

0:47Innovative Data Solutions helping use five Trend better uh across that data ecosystem so I have uh personally been around in the data space for for a good while spent uh spent time at Oracle spent time at in had dup land with Horton works and the five to six years prior to five Tran I was in the data Consulting space with hashmap and NTT

1:09data and uh have really enjoyed uh these these last couple of years over at F Tran it was an easy transition for me because I was already recommending F Tran a lot to my clients and so it it was a natural transition to say hey all that speed Simplicity operational sustainability and self-service I've been talking about now I'm representing one of those products in the mix that

1:31does that so awesome so can you tell us can you tell us about five Tran what in just a few words what's F Tran doing and why our customer using F Tran maybe a few I don't know a few use cases a few example with a few customers would help to understand yeah absolutely it's think of FIV Tran very simply as an automated data Movement platform and and one of

1:53the things I I talk a lot about is I I love the fact that we focus on one thing I think the more you get you know kind of uh you move into different areas it can it can dilute your focus and our our CEO's been very clear that we have a very large remit to make data movement as simple as essentially turning on your lights you know don't let me don't make

2:15me think about it don't make me know what infrastructure is behind it don't don't make me configure anything and we've got over today 8,000 customers that are doing that very thing with FIV Tran across pretty much any source you can imagine databases applications file systems Event Systems uh you know you think about what's the toughest thing uh

2:38to me when you when you think about datab bricks Lakehouse I've got so many incredible data workloads that the datab bricks Lakehouse drives everything from data warehousing to data lake lake house to uh data applications AI ml gen AI everything in between but I need there's a common theme that I need for each one

3:00of those Databricks Lakehouse workloads and that is high quality trusted usable data that's easily understandable and it's organized and it's really analytics ready and and to me that's what five Trend does exceptionally well again across any data source so the easy way to think about it is any data source any Databricks

3:22Lakehouse data workload all right sounds good and most of the use case would be around would it be more the analytics use cases or do are you also doing more real time like you know KF real time use cases yeah it's really anything I the way I put it is if you've got a workload that's fit for purpose for the Databricks Lakehouse chances are five Tran's going to have a really good fit with our

3:44sources because we have over 500 today across just about any category that you can imagine so uh a nice thing just like the just like Databricks we go across industry so you think okay can I can I help with uh personalization or fraud detection in financial services what about customer 360 or product recommendations in retail or

4:08cpg um Predictive Analytics or machine maintenance types of use cases in a manufacturing or industrial setting and again the industries just keep going much like Databricks we're not we're not limited by industry in fact you know you think about the types of data that that we moving into the lake house those those pretty much stay the same it's

4:30just how based on your industry you're going to apply it to each one of those use cases and what's that Databricks Lakehouse workload that you're looking to drive do I need to go near real time is it more um you know incremental over the course of an hour or a day and we can really adjust to all of those five Trends platform does everything from

4:53continuous sync all the way through if I want to schedule it every 24 hours I can do it that way as well and you have a range of deployment options too if I need to if I want pure SAS I've got Pure SAS if I want to deploy my data capture and data integration and data movement technology behind my firewall on Prim or in a

5:15private Cloud before I send it up to Databricks I can do that as well with f Tran self-hosted private deployment I I feel like you know sometime a lot of people can think that connecting to a new data source and ingesting some data looks super easy like I can open a connection to my SQL I can open a connection to Salesforce and just D the data what's the challenge here why do I

5:38need something like five Trend to ingest my data yeah it it's it's tough I you know I go back to the uh the days of Hadoop like I said and and long before that and I think you know I always like to think about can I if I'm especially if I'm an Enterprise can I get standardization and predictability in what I'm doing from a data pipeline standpoint point because

6:00it is the first step to getting to those data outcomes and it's it's more than just building when I ask consulting firms you know large gsis and Si I work with those those types of Partners a lot and say how many Sprints does it take to build a single data pipeline a single data pipeline generally the answer is somewhere in that four to eight Sprint range so you're talking eight weeks to

6:2216 weeks to design it build it test it deploy it automate it document it maintain it and then once they get really you know honest with me they go you know what Kelly actually it never ends because something's always changing that that DML or ddl on a database you know A A A change is happening in my

6:44Salesforce application my workday application my service Now application I'm getting files added to um you know an object store like S3 or something like that that I want to move all this data into Databricks how do I handle all those changes to me that's one of the biggest things is refactoring for those edge cases and handling that change detection it is exceptionally

7:07hard to do at scale and you try doing it for let's say at an Enterprise a hundred different data sources for for one one one single type of data source do that a 100 times the amount of people that you have to have is just uh incredibly High and the time devoted to that is incredibly high I would rather my data engineering team and my application team

7:29be focused on Downstream building uh really high quality data uh use building out use cases in The Lakehouse off of those workloads and not worrying about keeping pipelines up and running and you know kind of keep my fingers crossed and hope nothing breaks uh syndrome so we're going to get you out of that to give me two more weeks two more weeks two more

7:51weeks to build the pipeline just one more Sprint I think everybody's been there and uh we can build uh we create these Pipelines exceptionally fast we'll see that today as we as we move into the demo yeah and I can relate to that I know I know one project and it was kind of just dumping a lot of post gray um database into into their air bricks it looked easy at the beginning

8:14and I think it took them many many Sprint like as you just said like it was way too long just to inj some data and if I friend could have done that in you know just when one like when day or so yeah I I always like to think if if we can if when we pair the power of Databricks up with five Trend and let's say our you know let's say there's a consulting firm or let's just say it's a

8:37client that's picked this up you know we have Databricks has done an incredible job working with fivet Tran on things like partner connect where from the Databricks console I can connect directly to FIV Tran automatically spin up a warehouse that FIV Tran is going to use my users my roles my permissions all those kind of things there's really nothing to it I'm moving data in a very short period of time into the Lake

8:59County so a lot of little things like that I think that can accelerate that time to outcome speed to impact whatever you want to call it I need to reduce the I need to reduce my data product and service backlog and I think taking care of the front end with data moving in is really important in order to be able to do that all right so why don't why don't we give it a try H you have you have a

9:21demo already that we can uh used to see how to connect to their bricks using the partner connects yes I do I I love demo that's one of my favorite things I'm going to it's okay Quinton I will share my entire screen all right so I've got I may refer to a couple of slides as we go through here I think what I'm going to look at showing today here let

9:43me go full mode on this is we'll set up over the course of maybe the next 10 minutes we'll set up an sap Erp connector and a sales force connector using FIV Tran to the Databricks Lakehouse and then and you you gave me a tip a few weeks ago when we were talking say hey Kelly check out Databricks Lake View dashboards so I

10:05love Lake View dashboards I I am totally sold on that so may try toh attempt a quick Lake View dashboard over some of that data that we just moved into the Databricks lake house so that's what we're going to do and and like you you talked about while or I talked about and you ask about like cross industry uh sap Salesforce those are cross industry and your industry use cases outcomes and

10:28those data outcomes that you're delivering are going to be very very specific to those Industries I have three represented up there but that could be insurance that could be media and entertainment that could be energy you you name the use case all right so what I've got up here couple more tabs I've got uh the five I'm going to hide that so we're we got a clean screen there got the five Tran UI up here I've

10:52also got uh Databricks uh workspace up here as well I'm uh Databricks and FIV Tran both support azure AWS and Google uh happen to be running data breaks in Microsoft Azure here and one thing I'll point out with five Trend I've got multiple destinations I'm not limited by just having one destination so in this

11:14MDS datab bricks H account Hands-On lab account I've got a datab bricks lab Unity catalog lab Unity catalog serverless lab and you can see how that's configured here if I go in I've got uh def uh determining what uh use see what Unity catalog I'm using where I want to process the data all those kind of things are really left up to the

11:37customer to the end user the other thing you can see here is I've got a lot of existing connectors uh here across those different destinations and we're going to go with uh sap Erp on Hana today in Salesforce and we're going to set these up and then I may come back a little bit later on Quinton and talk through some of the um you know some of the unique uh

11:59features as well but I want to get these set up for us and get that moving so first thing if I've got multiple destinations configured for this account five trainers going to say okay Kelly which one of these destinations with Databricks do you want to move data to so I'm going to go with the unity catalog serverless destination is what I have it named and one thing I like to point out I mentioned 500 plus it's

12:21actually significantly over 500 we're at 545 sources out of the box today across databases applications files and Event Systems I think I'm going to go what do I want to do first today let's go we'll go Salesforce today uh I will set up this Salesforce

12:44connector so this is an application connector we'll do SAP Erp second uh this Salesforce connector I can name my destination schema anything I want if I come out here what I mean by this is if I come out here to Databricks you see that my Unity catalog here and you can see these um Databricks Destin uh sorry Databricks databases or schemas that are out here we're going to name this

13:09one what do we want to call this we will go uh predictable uh Salesforce to Databricks I don't so it's going to create this schema if it doesn't I don't you're exactly you you don't see if you look at this under this TS catalog demo um catalog I do not have anything that says

13:31predictable here so I want to do something totally different FIV trend is not only going to create the schema create all the uh tables create all the columns for you initially I don't have to do any of that in Databricks first uh five Tran is also going to manage all the schema drift associated with uh Salesforce as well so um all that stuff

13:53is handled for me automatically I go out five trend for application connectors five Tran is doing an API integration and I always always like to tell folks that you know every single application Salesforce service now workday you name it is going to have a little bit different um way that they enable you to interact with that API so we try to

14:16take advantage as much as we can of the capabilities that an application like Salesforce gives you give you we do things like uh we even handle formula fields and and so forth so save and test that you can see I was able to authenticate I'm going to connect to that API and this had this uh says Hands-On lab account this is five Tran

14:38this is we're setting up a production pipeline here so our connection test passed very predictable and standardized in terms of how five Tran is going to interact with really any application I'm going to get a list of these uh and I'm I'm going against by the way Salesforce developer instance I've got over a thousand tables in this developer instance I do not want all all of those

15:00but I would like to take 11 tables and there's a there's a very specific reason why I want to take 11 I don't want to do all 1100 because we'll probably uh run out of time as that massive uh data set over the next 10 minutes is moving data over I don't have that kind of time but I'm going to put 11 tables in here

15:22because I want to show some out-of-the-box Transformations uh using a great uh bricks partner DBT as well as a great FIV Trend partner and I'm going to put these 11 tables in here the reason I'm saying specifically 11 is the models that we're going to build off of these tables are

15:44it requires all 11 so we're going to enrich and transform and build things like uh average bookings total bookings uh lead time all that kind of stuff so give me this is the longest part of this demo is just picking these out individually I got a couple more task user and user rle and that so that's goinging all the

16:07the Salesforce API so it means you don't have to install any agents no no nothing installed it's great question you're calling the Salesforce API and you can see I'm going to block all other uh every other table here and let me show you what I got I have 11 tables here everything else is blocked and you notice it says review the connector schema below and select columns to block or hash I can even hash at the column

16:33level if I want to for an individual table if I say hey look all right in my Salesforce Source it's okay if I've got I don't know a driver's license number or some sort of pii type column but I don't I don't need that in my uh Databricks uh Lakehouse data product that I'm building I I don't want to have that there I want to keep that in the source so I can actually hash at the column

16:57level we also have right now Quenton in private preview we have Auto pii detection which is pretty cool so it will flag me and say hey Kelly before you send this data over to the to the Lakehouse are you sure you want to do that or even after it's moved if it sees some combination I'll say oh wait a minute are you sure you want to do this and I can make make some changes here so

17:20right now I won't hash anything I've got 11 tables let's save and continue the last thing is this is pretty cool so five Tran's going to move this 11 table data set over for me uh with Salesforce but okay so okay great I've got my initial data set my historical sync what about CDC what about ongoing replication what about when something changes in a new schema a new table new column is

17:42added do you want that Kelly do you want some of it or do you just are you happy with this data set you want to block that and this is this is part of uh your decision making around your CDC and incremental change capture I can determine that here I'm going to hit continue and and I'm going to say yeah let's go ahead Okay cool so I've got I've got a Salesforce connector I've got

18:05some outof the-box models that five Tran will build yeah go ahead and build that That's All She Wrote off to the races start that initial sync and I want to go back to setup real quick I mentioned incremental I can go down to one minute syns on Salesforce I can go up to 24 hours and anything in between I had had somebody quit and ask

18:27me uh that was a wild hey Kelly I need to go beyond 24 hours well you know 24 hours that's that's a like usually people want us to go faster than 24 hours you know give me down to ad minute I've never really had it that one person said I need I like every week every 48 Hours said well sorry you're good 24 is the best we're GNA be able to do there

18:49so this is going to uh okay so we're now that is you have a uh an active pipeline that is you can see here this predictable Salesforce Databricks pipeline that is moving data over to the unity catalog server list Databricks instance let's add a an sap connector here quickly and then we're going to go check

19:11this out I got to bring up my sap credentials here so give me a moment there we go all right I've got a separate screen on that so sap not the not necessarily the easiest source to deal with right I mean you've got over a 100,000 and tables it's hidden behind a netw weav application layer you've got

19:33all kinds of um incredibly Rich data sitting in sap Erp you've got multiple applications you got ECC uh that ECC instance could be running uh with Hana could be running with Oracle could be running with SQL Server could be running with db2 or something else or you might have sap S4 Hana and uh the latest and

19:56greatest with sap I need to be able to get to that dat I might also have some sap applications like concur or other SAS applications and F Tran has a range of application uh or connector options I should say for sap today what I'm going to show you is the sap Erp on Hana connector this connector Quenton uh

20:18leverages a remote function call that that goes through the uh netw Weaver application layer okay so we're not accessing the database Direct we're going through a netw weaver remote function call we've got an sap transport uh five Tran transport that runs on that sap uh netw Weaver layer and that's this uh five Tran netw Weaver API download

20:41that you see if you've got an sap um basis team they are very familiar with this this is the way that sap communicates with the external world and and one of the preferred ways to do that so you'll notice I've got an application server and I'm going to copy and paste these things so I don't mess them up

21:04I've got an my net Weaver application server host identifier I've got my Cy number client number my user that looks good RFC HF uh hiva and then I'm going to copy my password in here also can connect uh I can connect with this uh sap Erp on a Hana connector I can go SSH reverse SSH

21:27or VPN today I am going to go SSH and that's that's where you see the value of f TR right good luck trying to do that by yourself you know you you want optionality here too you might have customers that want to connect a particular way or have particular ways

21:49that their Network and security teams connect and you go well I developed this you know SSH thing for a customer but this next customer needs VPN those things are really hard to do on a oneoff basis so we do try to get just like you saw with all those sap connectors depending on your database and every you got a lot of optionality there so let me look oh you know what I didn't

22:12do is name my destination schema so let's go fast sap Erp to Databricks and I think that all looks pretty good let's save and test I'm going to connect to that okay yeah so I'm going to confirm my SSH fingerprint that's cool and I should run through the rest of my tests here I love with five TR SAS

22:36I I love to see the big green box there that says hey yes all your connection tests have passed that's a great sign like things are set up on the back end you are ready to go boy you don't deploy any infrastructure with uh five trans SAS at all this is a little bit different than Salesforce now remember there's there's no thousand tables in sap it's 100,000 tables so the way we did this we said hey search for the

23:01tables that you want to use for your analytics product so in this case I'm going to grab a couple I could grab anything I want I'm going to grab uh some a couple of tables around material data within sap let's save and continue the Mt and Mara tables we'll move those over to the Lakehouse and again that's it I'm starting that initial sync I've got uh

23:24that rolling I'm going to go back out quickly to my uh Salesforce so let let me you ask you a question like the we see that with the UI what how do you do it in real production uh deployment do you set up everything with the UI do you call Api do you use something like theform you to set up the infra how is it working yeah

23:46you you've really got options it's it's actually a really good question let me go out I go out to our docs a lot and if you go out to our docs page you see this API reference we have an open API that you can see all the different areas that you can use to uh enable additional automation as well we even have a things

24:10like a terraform provider um we integrate with things like prefect and airflow so it I'm going to say Quenton it depends on your use case and it depends on the you know if I've got a thousand SQL Server database connectors I don't probably want to set those up individually yes I would want to automate that if I'm doing individual

24:32connectors uh I've got a couple of sap I've got a work day I've got a service now I've got an S3 or whatever it may be then those it's you could tell I mean it's very fast to set these up so you have individual connectors that have a lot of the same connector I would say go for more automation if if it's more oneoff then you know just setting it up just it doesn't take take long at all to do that does that make sense y makes

24:57sense see if we got this uh I was going to sync this one more time I want to pick up uh Transformations here so let's go out go out to what did we I want to see what did we call these we've got let's see last sync okay we've got predictable Salesforce I think the other one I called Fast the

25:20sap was it fast yeah this one right here which is sinking right now fast sap Databricks and we'll come back and look at the metrics here in a minute so predictable is the first one so I'm going to ref I'm in my catalog Explorer we don't see predictable we don't see fast in these schemas here let me refresh my catalog Explorer there is well there's fast

25:43right there fast sap Erp there's MKT there is oh by the way I love this uh these suggestions on describing your tables I have used this quite a bit since it came out in Databricks I'm going to accept these are awesome I'm a I'm a huge fan of uh how uh Databricks

26:05is leveraging AI to quickly enable me to describe uh these these tables that I have I'm going to hit accept on that one too because I I know what these are they're they're they're very very uh very very solid so I've got uh sample date out here and whatever is in that uh sap uh table that Mar and MKT uh MKT

26:27table that's what you're going to get out here it's going to be in you know typical relational format columns and rows and tables and then I can go and do my enrichment Transformations whatever I want with those at that point um these relatively small data sets obviously for you know purposes of doing this over 10 minutes but here's that uh

26:50sap uh predictable Let me refresh this one more time yeah okay so predictable Salesforce datab break remember we had those 11 tables account contact event then we also kicked off that quick start um that transformation I told you that quick start transformation if I go out to five Trend

27:13DBT packages right here if I come out here I've got qu over 100 DBT open source packages out of the box that I can use that five Tran is development said hey open open source Community customers prospects Partners feel free to use whatever you want and I've get these out of the box models

27:37that are built that give me like on this Salesforce sales snapshot things like average bookings average days open average days to close and that's what I want to show you the last thing I want to show you on this uh for the for the integration with Databricks is I've got all these models built I didn't actually have to do any transformation work the models came out of that for me so if I

27:59go out to dash boards go ahead so wait wait so was it running on on five Trend the DBT transformation no no no no those DBT Transformations run in the Lakehouse fantastic question all we're doing is scheduling and orchestrating those that is native DBT core or DBT Cloud running in the Databricks Lakehouse we don't we

28:22don't run any Transformations outside of the Lakehouse zero so you just select the DBT package and run it from it's going to Trigg out the Jing air breaks that's right that's right for the let me show you this uh quick start data models we don't this is not for every single uh connector so I've got quick start data models for pretty good range of

28:45connectors here though these are the ones that are basically just click one button and voila I've got analytics ready data in The Lakehouse so that was Salesforce that we used here obviously a relational database that's those are going to be very custom so I don't have you know quick start data models for that but there are if I look out here in

29:08let's go back up to the DBT package Hub uh I've got I've got an sap package out here it's not a quick start but if I have DBT core or DBT Cloud running uh in my environment F Tran can kick off the on once new sap data flows into the Lakehouse FIV Tran would automatically kick off those those DBT models to run

29:30and so you always have the freshest data Downstream um because those are running automatically in The Lakehouse once that new data is persisted in so that's pretty cool um all right I want to I'm going to go out to dashboards here real quick and this is this I again huge shout out to you for pointing out Lake

29:52View to me because I've been using the other dashboards which are awesome but I like the lake view dashboards let's let's create a lake view dashboard here and the first thing we're going to do I all I've got to do on this so on the in the other dashboards you know we're going to run some queries and and kind of build uh standard here I can actually select a table now I'm going to

30:14select let me select a catalog we're in that TS catalog demo and then I think I can select a schema here too I'm trying to remember what I wanted to select here what was my sales for uh predictable Salesforce yeah I think it was that one right there I think we called it predictable so here's all my models out

30:39here those are those are staging models I may not have that persisted out let me jump out here let me use I'm going to use one of these other ones because I don't know if those models have fully generated let me get out this is the same exact uh connector here let's go sales

31:01snapshot see what happens see what happens if I do this for sales snapshot oh yeah there we go so that is that actually is that predictable um remember we called it

31:24predictable predictable Salesforce Databricks that that was it and look look what it did Quinton so these are this is my aggregated Salesforce sales snapshot model that gives me bookings amount Clos this month total bookings amount closes quarter total bookings so I'm going to take two of these we'll take total bookings and total bookings amount and let's come out here this is the coolest

31:48thing I really I really like this let's build a visualization describe a chart total bookings okay I like that I got I know that's I know that I use this data set a lot I know that's the right number that is the coolest thing ever 3.65 million in total bookings let's do

32:12one more quick one here it looks so easy I mean from the to the transformation go are you kidding me average okay average bookings didn't like that one let's see um let me go I'm let me go back out my data let me make sure what I've got here let's see what do I

32:35have oh I bet if I do this let's see if that if that does it yeah there we go average bookings bang so that's that I know again that is a correct number that 202 I'm going to accept that and then if I want to come over here pop a title on that thing all right average bookings

32:58and then over here let's pop a title on that one total booking so I don't know I'm not quite sure how long you and I have been talking uh probably you know 10 or 15 minutes or so but but Quenton I'm going to go back out here we went from no uh connectivity to either sap or Salesforce we Ed five Tran to persist

33:20that data into the lake house and we built a very quick okay quick it's simple Lake View dashboard with that counter uh with that counter visualization we can name our dashboard here this is uh yeah he could be could be anything you could have you know buy and and then and Sh anybody I'm ready to publish I'm off to the races and I can

33:42view my published dashboard there amazing and it's incredible so that's that's some of the power that you get with going I mean you think about this in terms of just showing uh showing a group within your organization a Le POC one of the one of the biggest things every time is how do I how do I become

34:05relevant in my organization and and and you become less relevant the longer the time is that things take especially to build data products you know two more weeks two more weeks two more weeks you can get these done very very fast with five Tran and Databricks and obviously I I would probably want to take this I'd want to do more transformation and enrichment in The Lakehouse but man to

34:27be be able to show data outcome impact quickly easily uh to me there's no easier way to do that than this combo of five Training Databricks yeah like the time to the time from inis to Insight is so small like it's it's really amazing it's exciting a a and I've got incremental change data capture set up automatically if changes

34:51happen in any of my sources those changes are persisted over on whatever schedule I set up and then yeah and then the dashboard we will refresh out of the boook because everything will be incremental what about pricing by the way any impact on the refresh and the pricing is it is it based on uh on freshness no we go we're we're continuing to work on how we uh uh

35:14handle pricing in the most optimal way today it's something called monthly active rows so it's changes that happen in that Source one time during the month that's kind of how we price now historical any historical sync is 100% free and 14 days of incrementals are also free we want to you know be able to get a baseline for what that uh

35:36incremental sync is going to be and then what the monthly active rows are going to be what what rows are changing during the month usually it's it depends on the data source if you have a highly uh High change type data source you know you might be up in that you know 20 or 25% of your total rows if it's not a data source that changes a lot maybe it's in that you know two to four 5% it just

35:58depends but um that's 100% consumption base that's how we do it is based off those changes on the rows during the during the course of the month so so wait so if I have like one terabyte of initial data the initial sync is free and then I 100% 100% free yeah every historical sync is free so all of those two that we just set up you know we took a small data set but yeah that's

36:20that's uh there's no charge for that at all man that's so good so nothing should you know stop anyone from using F TR now I I don't think so I you know I recommended when I was sitting in the Consulting chair for those five or six years I recommended five Tran 100 125 times and I I just feel like um regardless of the data source now and the data workload in The Lakehouse in the data brecks Lakehouse I mean' got

36:45what you saw there we've got a really really elegant easy seamless way to do that then if you do have maybe it's a financial maybe your financial services institution your Healthcare institution say okay as much as I would like to do SAS for my data integration I really want to deploy this behind my firewall we have five Tran selfhosted hvr private

37:08deployment for that so I can put all of my data integration infrastructure behind my firewall or private Cloud do that processing there and then send it up to the Databricks Lakehouse so we can cover pure SAS all the way to pure self-hosted and so again there's that optionality I think is really important and it becomes critical the larger the Enterprise is amazing well

37:32thank you kitty I think that was awesome now I want to try to use five TR you know as much as I can I hope that was at full and thanks so much for your time absolutely Quenton thank you so much I really enjoyed it thank you