All right. So, we've got our sales data

AI agent right here and it's hooked up

to all of these tools so that it can

query through our sales database. So

I'm, going to, open, up, the, chat, and, I'm

going to ask it how much total revenue

have we made from our Bluetooth speaker.

And, so,, what, it's, going to, do, is, use

these tools right here and it used the

product name query to look at all of the

times that we've sold a Bluetooth

speaker. And then it just used its

calculator tool to figure out that

total. So, it just finished up. If I

open up the chat, you can see we got the

answer. Total revenue from Bluetooth

speaker was $546.

It calculated this by retrieving all

sales rows for Bluetooth speaker and

then it summed each row's total revenue

and that's why it used its calculator

and got to the answer 546 which is

correct. And so you may have never seen

this node before. These are data tables

that are stored natively within n and it

looks like this right here. You can see

this is the data table and these are the

different products and the quantities

and price and revenue. And this is what

it just searched through in order to

answer our question accurately. And once

again, these are natively in edit. So

our agent didn't have to go over the

internet or make an API call somewhere

to get to this data. It lives right here

within our environment in a new tab

right here called data tables. So that's

what, we're, going to, be, talking, about

today. As always, you guys will get this

workflow as well as the two example data

sets for completely free so that you can

get in here and get your hands dirty and

just start playing around with it to

understand how it works. But let's get

into the video. So here's what the data

tables tab looks like in Nitn. I'm

recording this video before it's been

actually rolled out to everyone. So if

you don't see this right now, all you're

going to, have, to, do, is, make, sure, you

update your Nitn to the latest version.

And then you should see data tables.

It's right here in my home screen. I've

got my workflows, my credentials, my

executions, and then I have my data

tables. And all I have to do to create a

new one is click on this little drop

down, click on create data table. I'll

call this one demo with a backslash or

maybe a forward slash. And then what we

have is two default columns which is a

created at time and an updated at time.

And then if we want to add different

columns we would just type in over here

to add a column. And then we have to

choose a data type. So we have string

number, boolean or date. Once I add that

column, I'm then able to add a row and

then I can just fill in the data right

here. Or I meant to type Nate actually.

Or instead of manually filling stuff in

you can import other data sheets in NAD.

So let's say I have this contact sheet

that I'm using in Google Sheets, but I

want to move it to NADN. Let me show you

guys how we do that. So here we are back

in Nitn and we have these orange nodes.

You would just hit tab or click on the

plus in the top right. And if you search

data tables, you can now see we have

this new node which says permanently

save data across workflow executions in

a table. And if I open that up, we have

the functions that are pretty similar to

Google Sheets like deleting a row

getting a row, inserting a row

updating, or upserting. So, just to show

you guys how this works, we're going to

pull in my contacts from this Google

sheet and we're going to import them

into this contacts data table in Nit.

So, real quick, I'm just going to delete

all of these rows. And keep in mind, you

can see down here, the last row starts

at ID 20. And when I delete these, it's

going to look like it resets, but when

we, write, in, more, rows,, it's, going to

start at 21 because this data table just

remembers. But that's a very minimal

detail. Like I said, let's just run this

real quick, and then I'll show you

what's going on. So you can see that

happened really really quick. We're

pulling in all of the rows from my

contacts sheet and then we're writing

them into a data table and you can just

choose from a list. You'll notice

there's no credential section because

we're not going to the internet. We're

just natively grabbing this from Nadn.

And then we just have to map our

columns. So the name I dragged over

name, the email number, notes, all that

kind of stuff. You just have to make

sure that the data types match up.

Otherwise it won't let you insert these

values. So now that we've done that, if

I go back into my end data table for

contacts and I hit refresh, we should

see those contact rows have come into

our instance. So hopefully you guys can

see how this opens up a few

opportunities. We're not going to be

able to dive into all the use cases

today, but let me show you guys one that

you could think about. So we've got our

contact data in NADN. And so what we

could do is basically use something like

this where where we're going to respond

to an email. But before we respond to

the email, we want to look up that

contact and filter through all of our

data, all of our contacts for just the

one we're looking for. So I'm going to

pull in a new email from our Gmail

trigger. We can see the email that this

is coming from is upitiggmail.com.

And we have Nate Herklman. So what I'm

going to do next is we're using a get

rows from data table node, but we're

using a filter and we're saying we only

want to pull back rows that match this

specific condition. And the condition is

that the email must equal the email that

triggered this whole flow over here. So

I basically just came down and dragged

that right in here. And now when I

execute this, we're only going to get

the row for Nate Herklman with that

email. And now what I can do is I can

feed the notes right here about this

person into the AI agent that's going to

make the email. So if I go to the next

step,, I'm, just, going to, execute, this

real quick, but let's look at what the

user prompt is. So the AI agent is

looking at the user prompt which is the

email subject, the email body, the name

of this person and the notes about this

person and this stuff is coming from the

data table. So it can see that the

subject was I need help. The email body

is hey I need help with my business. Any

suggestions from Nate Hkelman who is a

founder into AI automation likes detail

cautious on commitments. Um this

actually pulled from chat GBT about me

when I said hey I need a quick notes for

a contact database. So interesting.

Anyways, then of course our system

message is just to write a friendly

email that's personalized. And you can

see it responds by saying, "Hey Nate

thanks for reaching out. I'd love to

help you with your business. Since

you're into AI automation and appreciate

a detailed approach, we can explore some

tailored solutions that fit your needs

without making you feel rushed into any

commitments. So, like I said, super

super simple use case, but the key here

was showing you guys that you're able to

filter and have conditions when you're

pulling back rows from your data set

which is going to be very important as

your database grows and grows.

Otherwise,, you're, going to, be, passing, in

hundreds, potentially thousands of rows

into the AI, and all you're doing there

is adding more tokens, which may cause

the AI to hallucinate a little more. And

the main reason is that it's just going

to be more expensive. And of course, it

doesn't have to be this. We took the

linear deterministic approach here, but

you could also add data table tools like

we'll see in a future example where you

can choose from a certain data table and

then you can have all these rows like

inserting stuff, filtering through

things, adding conditions, updating

rows, deleting rows. So everything that

you would typically do with Google

Sheets, but now it is quicker and it's

in Naden. Cool. So let's go on to a

different example here. Let me just grab

my manual trigger and move that over

there. So now we're going to do the

sales data. So I've got a sales data

sheet right here in Google Sheets. You

can see it's 20 rows. We have three

total products to look through. We have

three total days to look through. We've

got prices, quantities, and total

revenue, stuff like that. And we're

going to, first, of, all, get, this, into, our

NAND data table. So in my sales data

table and NN, I'm just going to go ahead

and make sure we delete all of these

rows. I want nothing in here just to

show you guys how easy it is to write

everything in. So back in our workflow

all I have to do now is execute this

workflow. We're going to pull in all 20

rows and read them or sorry and write

them into our data table in NEN. So I

should be able to refresh this. And now

we see our sales data. And then because

we have this AI agent hooked up to it

and we have different tools, it should

be able to answer the majority of our

questions by using a combination of

these tools and the calculator. So let's

do a few examples and I'll show you

what's going on within the tools. All

right. So, if I ask it this first

question, which is how many products

were sold on September 15th this year

what it should be doing is going to this

date query tool, and it should only pull

back rows where date equals September

15th. And then it's probably going to

use its calculator to actually make sure

it's doing that math correctly. So, you

can see it got the answer of 10, which

is correct. And you can see what it did

is it retrieved all sales records for

2025, 9,15, which is September 15th.

There were six total transactions, and

it summarized them all using a

calculator. So it was 2 + 1 plus 3 blah

blah blah which equal 10. So if I click

into the data query tool that it decided

to use, you can see the reason why this

works is because we have the condition

which is the date sold equals the value

that the AI fills in. And the AI filled

in the value right here, which is

exactly how we see it in our sales data

table because we told it this is

basically the format of dates. And one

thing I wanted to call out real quick is

that you can see this is set up as a

string. It's not the actual date data

type. And the reason why we did that is

because in our sales data in Google

Sheets, it was formatted like this. And

the hyphens basically make it a string.

So that's why it's a string here. But

anyways, like I said, it found that here

through here all had date equal 9:15 and

then it just added up these quantities.

So let's do another quick example. How

many wireless headphones have we sold?

It's going to do a product name query

because it has the product name. There

you go. You can see it just hit it. I

can actually click into this real quick.

You can see it filled out the condition

right here as wireless headphones. So

that's what it's looking for. We could

go ahead and verify that all of the rows

that it pulled back, you can see the

product name has to equal wireless

headphones. And then what it did is it

would just add up all of these

quantities sold. And it got the answer

12,, which, is, correct., All right., And, for

one last question, this one's a little

trickier. What's the average number of

product ID BS002

that we're selling per day? So, it used

the product ID query and it's basically

going to look at all three days, figure

out how many were sold on each day, and

then find the average of that. You can

see it just hit the calculator. So, I

think it's about to give us our answer.

The answer we are looking for is four

and we're about to get it. Okay, so the

summary for product ID, it says data

used all sales rows for this product ID.

Total number sold was 12. There were

three different days, so 12 divided by 3

is four units a day, which is correct.

So, I know we're going a little bit fast

through this, but you'll be able to

download this entire workflow, as well

as all of the Google sheet resources

that you need right here for completely

free. All you have to do is join my free

school community. The link for that is

down in the description. And when you

get there, you'll go to YouTube

resources. And when you find the post

associated with this video, let's say it

was this one, you can see that there are

JSON files here, and you just need to

download those and import them. And then

there'll also be links to make a copy of

these spreadsheets if you want to play

around with them in Naden data tables.

And the reason I bring that up is

because I'm not going to dive into this

entire system prompt, but I'll just show

you real quick what I said. Basically

you're a master sales data analyst. You

have these tools. Here's what you use

each one for. And then what I did was I

gave it the valid names that go into

each category. So for product names, I

gave it the three products. For the

date, I told it the format that it needs

to structure its dates as. And then for

product IDs, I gave it the three product

IDs as well, just so for the sake of the

example, we could see how the filtering

works. Okay, so for the last example

that we're going to do is I just have a

code node right here that basically just

outputs 400 items. It just counts from 1

to 400. And what I want to do is we're

just going to see the difference in

speed because of the fact that we're not

going over the internet to make this API

call. So, we have this sheet right here

just called test and it just has one

column called number. And all I'm going

to do is we're going to execute this and

then, we're, going to, see, how, long, this

node takes to write 400 rows in. So

we'll execute it. We can see it's

spinning, it just wrote in all of the

data. So, we can see if I scrolled all

the way down to 400, we can see there it

is. And now, if we go into the node, we

can see right here that it executed in

2,00 milliseconds. 280. So 280

milliseconds. Now watch what happens

when we connect it to our nit end data

table. Connected to a data table called

test. We're writing in the same exact

data. So when I execute this one, you

can see

oh that honestly seemed pretty similar.

Let's see how that was. That was 2511

milliseconds, which is really

interesting because almost all of my

previous examples the data table has

been faster. Okay, so that was pretty

interesting to me. We're going to do it

again with 60. My hypothesis was maybe

when it's less, Google Sheets always has

kind of like more time, but with bulk

functions, it's quicker. I don't know.

We'll see. So, we're going to run this

with 60. Google Sheets just took 1,700

milliseconds. Now, we're going to go to

our end data table, run it again, and

that was a lot quicker that time. And

this one took 300 milliseconds. And now

just to confirm my hypothesis one more

time, we're going to do it again with

20. So 20 to Google Sheets took us a

total of 1,700 milliseconds once again.

So that might just be kind of like its

floor. But when we go to end data

tables, that was instant. Like that took

97 milliseconds. So that was

interesting. If you have tons and tons

in the hundreds, it's going to be

similar. Naden may even be a little

slower, but with the majority of your

writes and especially if you're doing

one row or two rows, which actually

let's just try it real quick. Sorry

this is just too fun. So we're going to

try two rows. Google Sheets has taken a

minute. It took 1,600 milliseconds. And

if we go to end data tables, that's

going to be instant. That took 11

milliseconds. So you guys can see when

you have less, the end data tables is

going to be basically instant. But if

you have a large number, like the first

time we did 400, it's going to be pretty

comparable. So yeah, hope you guys found

this one interesting. Just wanted to

make a quick video showing you guys how

you can use these data tables and kind

of the difference between using

something like these and Google Sheets.

Another thing that I was thinking of is

you may run into rate limits with Google

Sheets because you're making an API

call. So if you're spamming it too

often, you may actually get rate limited

and you might have to work in like

weights or retries. But I don't see that

happening with end because once again

it's all staying internal. So don't

forget you guys can grab this whole

workflow as well as the two sets of test

data if you want to just play around

with stuff so that you can basically

mimic this workflow and see how it

works. All you have to do for that is

join my free school community. The link

for that's down in the description

completely free. And if you're looking

for some more structured guidance and to

take your learnings a little further

then definitely check out my plus

community. The link for that is also

down in the description. We've got a

community of over 200 members who are

building and earning with NAND. We have

a full classroom section with agent zero

for the beginners. We've got 10 hours

and 10 seconds where we dive into NAND.

And, then, we, have a, new, course, called

Oneperson AI Automation Agency, which is

a bonus for annual members. And it's all

about laying the foundation for building

a scalable AI automation business. So

I'd love to see you guys in this

community. But that is going to do it

for today's video. If you enjoyed or you

learned, something, new,, please, give, it a

like. Definitely helps me out a ton. And

as always, I appreciate you guys making

it to the end of the video. See you in

the next one. Thanks everyone.