Thank you for joining us today for this demo of Snowflake's UI.
My name is Peter Mebane, and I'm a sales engineer here at Snowflake.
Snowflake's platfrom enables a number of different workloads for users on top of any of the three major clouds.
We believe that you should have one platform where you can store all of your data regardless of its structure,
where you can provide managed, secure access to it, and leverage the unlimited scale of the Cloud.
Finally, we believe you should be able to do all of this without having to manage infrastructure,
so that you are only focused on getting value out of your data.
On top of the technological benefits that Snowflake provides, the idea of the Data Cloud is really the idea
that value in Snowflake comes from the content that you get access to as a Snowflake customer.
Because data sharing is so easy in Snowflake, you can not only monetize your data through the Data Marketplace,
but you can bring in external data to enrich your internal data sets without having to manage FTPs or go to cloud buckets.
You can get all of this data live from the providers with minimal management.
In this demo today, I'm going to give you a brief tour of the UI.
I'll show you how to load data into Snowflake; we'll talk about scaling compute warehouses;
and we'll also briefly touch on semi-structured data and how you can query that in Snowflake.
We'll talk about our Data Marketplace and overall data sharing features,
and we'll briefly touch on creating visualizations and dashboards in the Snowflake UI.
The data we'll be dealing with today is City Bike data. City Bike is a bike-sharing service.
We're going to be focused on data in New York City, and the data we'll be dealing with is about trips and stations.
We're also going to bring in some of our own weather data to augment these data sets.
In the Snowflake UI, you can see that I'm signed in with a DBA City Bike role.
I'll go through these tabs here. The first is the worksheets tab--and we'll get into what a worksheet is in a second.
We've got dashboards, which we'll also talk about later.
Under the data tab, you can see your databases, and you can drill into these further to see schemas, tables, views, etc.
This is also where you'll get access to the Data Marketplace.
The compute tab is where you're going to manage your warehouses.
At Snowflake, we separate storage and compute. Compute is what's actually doing the work on your data,
whether it's loading it or transforming it or clearing it. When you create a warehouse you'll give it a name,
then you're going to choose the size. An extra small is a one-node cluster.
And you'll see that, with each step up, the number of resources doubles.
Snowflake charges per second, so if you have an extra small running for one hour,
you will see that it consumes one credit.
We also have multi-cluster warehouse. In this situation, Snowflake will start off with one extra-large warehouse.
As users sign into the system and start using this warehouse, we don't want there to be resource contention
and the degradation of performance for those users.
With multi-cluster warehouse, Snowflake will automatically spin up an identical extra-large cluster
and balance the queries between the two.
It can do this up until 10 clusters, so that end users see no degradation of performance
regardless of how many users are accessing that warehouse at the same time.
And just as Snowflake automatically scales out, it will scale back in when those resources are no longer needed,
so that you aren't paying for them unless they are being used.
Let's get into the demo.
We're going to start off by loading some trips data.
So I've set up a stage where I've staged 232 .CSV files.
We'll see that this data as a whole represents about 2.2 gigabytes of compressed storage.
I'm going to create a table for this to land in,
and I'm going to go ahead and start copying data into this table.
You'll notice that I'm only copying data from 2020.
And if I look at my warehouse size, my load warehouse is a small,
so we'll see that we loaded 40 files in about 12 seconds.
This equates to about 20 million rows.
But if you remember, I had 232 or 242 files in that stage,
and I don't want to wait for all of those to load.
So you can see, within seconds I scaled this up to a large warehouse,
so I went from a small to a large, which is a 4x increase in compute resources.
Now I can copy in the rest of the data, and you should see that it runs in about the same time,
maybe a little bit longer,
and we're going to load in the remaining 192 files that were in that stage.
There we go.
We can see we loaded 192 files in 13 seconds.
Now that I'm done with that, I can scale the warehouse back down to a small
because I don't need the large resources anymore.
We see that I now have about 116 million rows in this table,
and if we take a look at the data, it's just data about the trips
--how long they took and where and when they started and stopped.
I've also got stations data that I've already loaded in,
and I've got weather data.
And what's interesting about this table is it only has two columns.
One is "V,"
and this holds the entire JSON file for this weather record
but what's really interesting in Snowflake is I can use basic SQL to query this.
So using a simple DOT notation,
where I'm saying go to the "V" column, go find the city key,
find the name key, and then cast that value as a string.
I can start pulling data out of this JSON file without actually changing the underlying file at all.
Now I'm going to go ahead and I'm going to change my warehouse to an extra large,
and I'm going to build a view that combines the trips data and the stations data together.
We can see that now, instead of just having the station ID,
we have information about where the stations are.
And now I can add in my weather data.
And, again, this data is still stored in its native JSON format,
but I can pull out the values I need into this view with basic SQL syntax.
So if I scroll over, we see we still have our trips and stations data,
but now we're also pulling in weather data, even though we haven't changed anything about the data,
as it's stored in Snowflake.
What we could also do is we could go to the Data Marketplace to find weather data.
So I could go to weather, I could find free data sets,
and I can bring these into my environment very easily.
I can click get data--I'm going to simplify this and just call it weather--
and I'm going to make it public for everyone to see.
Accept the terms and conditions, and that's it. It's all done.
So if I go back to my worksheet,
and I refresh the page,
you'll see that I now have access to this weather database.
And all of these views that I have access to,
I can query as if it's my own internal data, and I can combine it with data.
And, again, once I set up this share for the first time,
I don't have to manage this to keep it up to date.
When Weather Source updates the data on their side,
I will automatically see that updated data in my environment.
In Snowflake's new UI, we also have some light visualization tools.
You can see I ran a query here, and I got the results back.
On the right, we have some light visualizations, where I can do some data discovery and drill down.
And I can also build out some visualizations.
So here I'm just mapping the number of trips per day over time,
and I can change the bucketing.
So I can do it on a daily basis or a weekly basis or a monthly basis.
And then once I've created this visualization, I can add it to a dashboard,
where I can now see all these visualizations that are pertinent to the City Bike data on one screen.
Thank you for joining us today for this demo of the Snowflake UI.
If you have any questions, please check out our Snowflake documentation on our website.
If you want to learn more, I would highly encourage you to check out our webinars,
our hands-on labs, our demos, and our office hours with customers.
Have a great day!