Welcome to Snowflake! This entry-level guide designed for database and data warehouse administrators and architects will help you navigate the Snowflake interface and introduce you to some of our core capabilities. Sign up for a free 30-day trial of Snowflake and follow along with this lab exercise. Once we cover the basics, you'll be ready to start processing your own data and diving into Snowflake's more advanced features like a pro.

Free Virtual Hands-on Lab

This Snowflake Guide is available as a free, instructor-led Virtual Hands on Lab. Sign up for the VHOL today.


What You'll Learn:

If you haven't already, register for a Snowflake free 30-day trial. The rest of the sections in this lab assume you are using a new Snowflake account created by registering for a trial.

The Snowflake edition (Standard, Enterprise, Business Critical, etc.) and cloud provider (AWS, Azure, GCP), and Region (US East, EU, etc.) you use for this lab do not matter. However, we suggest you select the region that is physically closest to you and Enterprise, our most popular offering, as your Snowflake edition.

After registering, you will receive an email with an activation link and URL for accessing your Snowflake account.

Logging into the Snowflake User Interface (UI)

Open a browser window and enter the URL of your Snowflake 30-day trial environment that was sent with your registration email.

You should see the following login dialog​. Enter the username and password that you specified during the registration:

login screen

Navigating the Snowflake UI

Let's get you acquainted with Snowflake! This section covers the basic components of the user interface. We will move from top to bottom on the left-hand side margin.

snowflake navbar


worksheets tab main

The ​Worksheets​ tab provides an interface for submitting SQL queries, performing DDL and DML operations, and viewing results as your queries or operations complete. A new worksheet is created by clicking + Worksheet on the top right.

worksheets tab details

The top left corner contains the following:

The top right corner contains the following:

The middle pane contains the following:

The middle-left panel contains the following:

The bottom pane displays the results of queries and other operations. Also includes 4 options (Object, Query, Result, Chart) that open/close their respective panels on the UI. Chart opens a visualization panel for the returned results. More on this later.

The various panes on this page can be resized by adjusting their sliders. If you need more room in the worksheet, collapse the database objects browser in the left panel. Many of the screenshots in this guide keep this panel closed.


dashboards tab

The Dashboards tab allows you to create flexible displays of one or more charts (in the form of tiles, which can be rearranged). Tiles and widgets are produced by executing SQL queries that return results in a worksheet. Dashboards work at a variety of sizes with minimal configuration.


databases tab

Under Data, the Databases​ tab shows information about the databases you have created or have permission to access. You can create, clone, drop, or transfer ownership of databases, as well as load data in the UI. Notice that a database already exists in your environment. However, we will not be using it in this lab.

Private Shared Data

shared data tab

Also under Data, the Private Shared Data tab is where data sharing can be configured to easily and securely share Snowflake tables among separate Snowflake accounts or external users, without having to create a copy of the data. We will cover data sharing in Section 10.


marketplace tab

The Marketplace tab is where any Snowflake customer can browse and consume data sets made available by providers. There are two types of shared data: Public and Personalized. Public data is free data sets available for querying instantaneously. Personalized data requires reaching out to the provider of data for approval of sharing data.

Query History

history tab

Under Activity there are two tabs Query History and Copy History:


warehouses tab

Under Admin, the ​Warehouses​ tab is where you set up and manage compute resources known as virtual warehouses to load or query data in Snowflake. A warehouse called COMPUTE_WH already exists in your environment.

Resource Monitors

resource monitors tab

Under Admin, the Resource Monitors tab shows all the resource monitors that have been created to control the number of credits that virtual warehouses consume. For each resource monitor, it shows the credit quota, type of monitoring, schedule, and actions performed when the virtual warehouse reaches its credit limit.


roles tab

Under Admin, the Roles sub-tab of the Users and Roles tab shows a list of the roles and their hierarchies. Roles can be created, reorganized, and granted to users in this tab. The roles can also be displayed in tabular/list format by selecting the Table sub-tab.


users tab

Also under Admin tab, the Users sub-tab of the Users and Roles tab shows a list of users in the account, default roles, and owner of the users. For a new account, no records are shown because no additional roles have been created. Permissions granted through your current role determine the information shown for this tab. To see all the information available on the tab, switch your role to ACCOUNTADMIN.

user preferences dropdown

Clicking on your username in the top right of the UI allows you to change your password, roles, and preferences. Snowflake has several system defined roles. You are currently in the default role of SYSADMIN and will stay in this role for the majority of the lab.

The Lab Story

This lab is based on the analytics team at Citi Bike, a real, citywide bike sharing system in New York City, USA. The team wants to run analytics on data from their internal transactional systems to better understand their riders and how to best serve them.

We will first load structured .csv data from rider transactions into Snowflake. Later we will work with open-source, semi-structured JSON weather data to determine if there is any correlation between the number of bike rides and the weather.

Let's start by preparing to load the structured Citi Bike rider transaction data into Snowflake.

This section walks you through the steps to:

The data we will be using is bike share data provided by Citi Bike NYC. The data has been exported and pre-staged for you in an Amazon AWS S3 bucket in the US-EAST region. The data consists of information about trip times, locations, user type, gender, age, etc. On AWS S3, the data represents 61.5M rows, 377 objects, and 1.9GB compressed.

Below is a snippet from one of the Citi Bike CSV data files:

data snippet

It is in comma-delimited format with a single header line and double quotes enclosing all string values, including the field headings in the header line. This will come into play later in this section as we configure the Snowflake table to store this data.

Create a Database and Table

First, let's create a database called CITIBIKE to use for loading the structured data.

Ensure you are using the sysadmin role by selecting your name at the top left, Switch Role > SYSADMIN.

Navigate to the Databases tab. Click Create, name the database CITIBIKE, then click CREATE.

worksheet creation

Now navigate to the Worksheets tab. You should see the worksheet we created in step 3.

new worksheet

We need to set the context appropriately within the worksheet. In the upper right corner of the worksheet, click the box to the left of the Share button to show the context menu. Here we control the elements you can see and run from each worksheet. We are using the UI here to set the context. Later in the lab, we will accomplish the same thing via SQL commands within the worksheet.

Select the following context settings:


context role and warehouse settings

Next, in the drop-down for the database, select the following context settings:

Database: CITIBIKE Schema = PUBLIC

context database settings

To make working in the worksheet easier, let's rename it. In the top left corner, double-click the worksheet name, which is the timestamp when the worksheet was created, and change it to CITIBIKE_ZERO_TO_SNOWFLAKE.

Next we create a table called TRIPS to use for loading the comma-delimited data. Instead of using the UI, we use the worksheet to run the DDL that creates the table. Copy the following SQL text into your worksheet:

create or replace table trips
(tripduration integer,
starttime timestamp,
stoptime timestamp,
start_station_id integer,
start_station_name string,
start_station_latitude float,
start_station_longitude float,
end_station_id integer,
end_station_name string,
end_station_latitude float,
end_station_longitude float,
bikeid integer,
membership_type string,
usertype string,
birth_year integer,
gender integer);

Run the query by placing your cursor anywhere in the SQL text and clicking the blue Play/Run button in the top right of the worksheet. Or use the keyboard shortcut [Ctrl]/[Cmd]+[Enter].

Verify that your TRIPS table has been created. At the bottom of the worksheet you should see a Results section displaying a "Table TRIPS successfully created" message.

TRIPS confirmation message

Navigate to the Databases tab by clicking the HOME icon in the upper left corner of the worksheet. Then click Data > Databases. In the list of databases, click CITIBIKE > PUBLIC > TABLES to see your newly created TRIPS table. If you don't see any databases on the left, expand your browser because they may be hidden.

TRIPS table

Click TRIPS and the Columns tab to see the table structure you just created.

TRIPS table structure

Create an External Stage

We are working with structured, comma-delimited data that has already been staged in a public, external S3 bucket. Before we can use this data, we first need to create a Stage that specifies the location of our external bucket.

From the Databases tab, click the CITIBIKE database and PUBLIC schema. Click the Create button, then Stage > Amazon S3.

stages create

In the "Create Securable Object" dialog that opens, replace the following values in the SQL statement:

: citibike_trips

: s3://snowflake-workshop-lab/citibike-trips-csv/

Note: Make sure to include the final forward slash (/) at the end of the URL or you will encounter errors later when loading data from the bucket. Also ensure you have removed ‘credentials = (...)' statement which is not required. You can also comment it out like the picture below by using ‘–'. The create stage command should resemble the below picture or not include the 3rd line.

create stage settings

Now let's take a look at the contents of the citibike_trips stage. Navigate back to the Worksheets tab and open the ‘CITIBIKE_ZERO_TO_SNOWFLAKE' worksheet we made, add the following SQL statement below the previous code as shown in the below picture and then execute:

list @citibike_trips;

In the results in the bottom pane, you should see the list of files in the stage:

worksheet result

Create a File Format

Before we can load the data into Snowflake, we have to create a file format that matches the data structure.

In the worksheet, again add the below command below the rest and execute to create the file format:

--create file format

create or replace file format csv type='csv'
  compression = 'auto' field_delimiter = ',' record_delimiter = '\n'
  skip_header = 0 field_optionally_enclosed_by = '\042' trim_space = false
  error_on_column_count_mismatch = false escape = 'none' escape_unenclosed_field = '\134'
  date_format = 'auto' timestamp_format = 'auto' null_if = ('') comment = 'file format for ingesting data for zero to snowflake';

create file format

Verify that the file format has been created with the correct settings by executing the following command:

--verify file format is created

show file formats in database citibike;

The file format created should be listed in the result:

create file format settings

In this section, we will use a virtual warehouse and the COPY command to initiate bulk loading of structured data into the Snowflake table we created in the last section.

Resize and Use a Warehouse for Data Loading

Compute resources are needed for loading data. Snowflake's compute nodes are called virtual warehouses and they can be dynamically sized up or out according to workload, whether you are loading data, running a query, or performing a DML operation. Each workload can have its own warehouse so there is no resource contention.

Navigate to the Warehouses tab (under Admin). This is where you can view all of your existing warehouses, as well as analyze their usage trends.

Note the + Warehouse option in the upper right corner of the top. This is where you can quickly add a new warehouse. However, we want to use the existing warehouse COMPUTE_WH included in the 30-day trial environment.

Click the row of the COMPUTE_WH warehouse. Then click the ... (dot dot dot) in the upper right corner text above it to see the actions you can perform on the warehouse. We will use this warehouse to load the data from AWS S3.

compute warehouse configure

Click Edit to walk through the options of this warehouse and learn some of Snowflake's unique functionality.

warehouse configure settings

Warning - Watch Your Spend! During or after this lab, you should be careful about performing the following actions without good reason or you may burn through your $400 of free credits more quickly than desired:

We are going to use this virtual warehouse to load the structured data in the CSV files (stored in the AWS S3 bucket) into Snowflake. However, we are first going to change the size of the warehouse to increase the compute resources it uses. After the load, note the time taken and then, in a later step in this section, we will re-do the same load operation with an even larger warehouse, observing its faster load time.

Change the Size of this data warehouse from X-Small to Small. then click the Save Warehouse button:

configure settings small

Load the Data

Now we can run a COPY command to load the data into the TRIPS table we created earlier.

Navigate back to the CITIBIKE_ZERO_TO_SNOWFLAKE worksheet in the Worksheets tab. Make sure the worksheet context is correctly set:

Role: SYSADMIN Warehouse: COMPUTE_WH Database: CITIBIKE Schema = PUBLIC

worksheet context

Execute the following statements in the worksheet to load the staged data into the table. This may take up to 30 seconds.

copy into trips from @citibike_trips file_format=csv PATTERN = '.*csv.*' ;

In the result pane, you should see the status of each file that was loaded. Once the load is done, in the Query Details pane on the bottom right, you can scroll through the various statuses, error statistics, and visualizations for the last statement executed:

results load status

Next, navigate to the Query History tab by clicking the Home icon and then Activity > Query History. Select the query at the top of the list, which should be the COPY INTO statement that was last executed. Select the Query Profile tab and note the steps taken by the query to execute, query details, most expensive nodes, and additional statistics.

history and duration

Now let's reload the TRIPS table with a larger warehouse to see the impact the additional compute resources have on the loading time.

Go back to the worksheet and use the TRUNCATE TABLE command to clear the table of all data and metadata:

truncate table trips;

Verify that the table is empty by running the following command:

--verify table is clear
select * from trips limit 10;

The result should show "Query produced no results".

Change the warehouse size to large using the following ALTER WAREHOUSE:

--change warehouse size from small to large (4x)
alter warehouse compute_wh set warehouse_size='large';

Verify the change using the following SHOW WAREHOUSES:

--load data with large warehouse
show warehouses;

resize context to large in UI step 1

The size can also be changed using the UI by clicking on the worksheet context box, then the Configure (3-line) icon on the right side of the context box, and changing Small to Large in the Size drop-down:

resize context to large in UI step 1resize context to large in UI step 2

Execute the same COPY INTO statement as before to load the same data again:

copy into trips from @citibike_trips

compare load durations

Once the load is done, navigate back to the Queries page (Home icon > Activity > Query History). Compare the times of the two COPY INTO commands. The load using the Large warehouse was significantly faster.

Create a New Warehouse for Data Analytics

Going back to the lab story, let's assume the Citi Bike team wants to eliminate resource contention between their data loading/ETL workloads and the analytical end users using BI tools to query Snowflake. As mentioned earlier, Snowflake can easily do this by assigning different, appropriately-sized warehouses to various workloads. Since Citi Bike already has a warehouse for data loading, let's create a new warehouse for the end users running analytics. We will use this warehouse to perform analytics in the next section.

Navigate to the Admin > Warehouses tab, click + Warehouse, and name the new warehouse `` and set the size to Large.

If you are using Snowflake Enterprise Edition (or higher) and Multi-cluster Warehouses is enabled, you will see additional settings:

warehouse settings

Click the Create Warehouse button to create the warehouse.

In the previous exercises, we loaded data into two tables using Snowflake's COPY bulk loader command and the COMPUTE_WH virtual warehouse. Now we are going to take on the role of the analytics users at Citi Bike who need to query data in those tables using the worksheet and the second warehouse ANALYTICS_WH.

Execute Some Queries

Go to the CITIBIKE_ZERO_TO_SNOWFLAKE worksheet and change the warehouse to use the new warehouse you created in the last section. Your worksheet context should be the following:

Role: SYSADMIN Warehouse: ANALYTICS_WH (L) Database: CITIBIKE Schema = PUBLIC

sample data query results

Run the following query to see a sample of the trips data:

select * from trips limit 20;

sample data query results

Now, let's look at some basic hourly statistics on Citi Bike usage. Run the query below in the worksheet. For each hour, it shows the number of trips, average trip duration, and average trip distance.

select date_trunc('hour', starttime) as "date",
count(*) as "num trips",
avg(tripduration)/60 as "avg duration (mins)",
avg(haversine(start_station_latitude, start_station_longitude, end_station_latitude, end_station_longitude)) as "avg distance (km)"
from trips
group by 1 order by 1;

hourly query results

Use the Result Cache

Snowflake has a result cache that holds the results of every query executed in the past 24 hours. These are available across warehouses, so query results returned to one user are available to any other user on the system who executes the same query, provided the underlying data has not changed. Not only do these repeated queries return extremely fast, but they also use no compute credits.

Let's see the result cache in action by running the exact same query again.

select date_trunc('hour', starttime) as "date",
count(*) as "num trips",
avg(tripduration)/60 as "avg duration (mins)",
avg(haversine(start_station_latitude, start_station_longitude, end_station_latitude, end_station_longitude)) as "avg distance (km)"
from trips
group by 1 order by 1;

In the Query Details pane on the right, note that the second query runs significantly faster because the results have been cached.

cached query duration

Execute Another Query

Next, let's run the following query to see which months are the busiest:

monthname(starttime) as "month",
count(*) as "num trips"
from trips
group by 1 order by 2 desc;

months query results

Clone a Table

Snowflake allows you to create clones, also known as "zero-copy clones" of tables, schemas, and databases in seconds. When a clone is created, Snowflake takes a snapshot of data present in the source object and makes it available to the cloned object. The cloned object is writable and independent of the clone source. Therefore, changes made to either the source object or the clone object are not included in the other.

A popular use case for zero-copy cloning is to clone a production environment for use by Development & Testing teams to test and experiment without adversely impacting the production environment and eliminating the need to set up and manage two separate environments.

Run the following command in the worksheet to create a development (dev) table clone of the trips table:

create table trips_dev clone trips;

Click the three dots (...) in the left pane and select Refresh. Expand the object tree under the CITIBIKE database and verify that you see a new table named trips_dev. Your Development team now can do whatever they want with this table, including updating or deleting it, without impacting the trips table or any other object.

trips_dev table

Going back to the lab's example, the Citi Bike analytics team wants to determine how weather impacts ride counts. To do this, in this section, we will:

The JSON data consists of weather information provided by MeteoStat detailing the historical conditions of New York City from 2016-07-05 to 2019-06-25. It is also staged on AWS S3 where the data consists of 75k rows, 36 objects, and 1.1MB compressed. If viewed in a text editor, the raw JSON in the GZ files looks like:

raw JSON sample

Create a New Database and Table for the Data

First, in the worksheet, let's create a database named WEATHER to use for storing the semi-structured JSON data.

create database weather;

Execute the following USE commands to set the worksheet context appropriately:

use role sysadmin;

use warehouse compute_wh;

use database weather;

use schema public;

Next, let's create a table named JSON_WEATHER_DATA to use for loading the JSON data. In the worksheet, execute the following CREATE TABLE command:

create table json_weather_data (v variant);

Note that Snowflake has a special column data type called VARIANT that allows storing the entire JSON object as a single row and eventually query the object directly.

In the results pane at the bottom of the worksheet, verify that your table, JSON_WEATHER_DATA, was created:

success message

Create Another External Stage

In the CITIBIKE_ZERO_TO_SNOWFLAKE worksheet, use the following command to create a stage that points to the bucket where the semi-structured JSON data is stored on AWS S3:

create stage nyc_weather
url = 's3://snowflake-workshop-lab/zero-weather-nyc';

Now let's take a look at the contents of the nyc_weather stage. Execute the following LIST command to display the list of files:

list @nyc_weather;

In the results pane, you should see a list of .gz files from S3:

results output

Load and Verify the Semi-structured Data

In this section, we will use a warehouse to load the data from the S3 bucket into the JSON_WEATHER_DATA table we created earlier.

In the CITIBIKE_ZERO_TO_SNOWFLAKE worksheet, execute the COPY command below to load the data.

Note that you can specify a FILE FORMAT object inline in the command. In the previous section where we loaded structured data in CSV format, we had to define a file format to support the CSV structure. Because the JSON data here is well-formed, we are able to simply specify the JSON type and use all the default settings:

copy into json_weather_data
from @nyc_weather 
    file_format = (type = json strip_outer_array = true);

Verify that each file has a status of LOADED:

query result

Now, let's take a look at the data that was loaded:

select * from json_weather_data limit 10;

Click any of the rows to display the formatted JSON in the right panel:

JSON data snippet

To close the display in the panel and display the query details again, click the X (Close) button that appears when you hover your mouse in the right corner of the panel.

Create a View and Query Semi-Structured Data

Next, let's look at how Snowflake allows us to create a view and also query the JSON data directly using SQL.

Run the following command to create a columnar view of the semi-structured JSON weather data so it is easier for analysts to understand and query. The 72502 value for station_id corresponds to Newark Airport, the closest station that has weather conditions for the whole period.

// create a view that will put structure onto the semi-structured data
create or replace view json_weather_data_view as
    v:obsTime::timestamp as observation_time,
    v:station::string as station_id,
    v:name::string as city_name,
    v:country::string as country,
    v:latitude::float as city_lat,
    v:longitude::float as city_lon,
    v:weatherCondition::string as weather_conditions,
    v:coco::int as weather_conditions_code,
    v:temp::float as temp,
    v:prcp::float as rain,
    v:tsun::float as tsun,
    v:wdir::float as wind_dir,
    v:wspd::float as wind_speed,
    v:dwpt::float as dew_point,
    v:rhum::float as relative_humidity,
    v:pres::float as pressure
    station_id = '72502';

SQL dot notation v:temp is used in this command to pull out values at lower levels within the JSON object hierarchy. This allows us to treat each field as if it were a column in a relational table.

The new view should appear as JSON_WEATHER_DATA under WEATHER > PUBLIC > Views in the object browser on the left. You may need to expand or refresh the objects browser in order to see it.


Verify the view with the following query:

select * from json_weather_data_view
where date_trunc('month',observation_time) = '2018-01-01'
limit 20;

Notice the results look just like a regular structured data source. Your result set may have different observation_time values:

query results with view

Use a Join Operation to Correlate Against Data Sets

We will now join the JSON weather data to our CITIBIKE.PUBLIC.TRIPS data to answer our original question of how weather impacts the number of rides.

Run the query below to join WEATHER to TRIPS and count the number of trips associated with certain weather conditions:

select weather_conditions as conditions
,count(*) as num_trips
from citibike.public.trips
left outer join json_weather_data_view
on date_trunc('hour', observation_time) = date_trunc('hour', starttime)
where conditions is not null
group by 1 order by 2 desc;

weather results

The initial goal was to determine if there was any correlation between the number of bike rides and the weather by analyzing both ridership and weather data. Per the results above we have a clear answer. As one would imagine, the number of trips is significantly higher when the weather is good!

Snowflake's powerful Time Travel feature enables accessing historical data, as well as the objects storing the data, at any point within a period of time. The default window is 24 hours and, if you are using Snowflake Enterprise Edition, can be increased up to 90 days. Most data warehouses cannot offer this functionality, but - you guessed it - Snowflake makes it easy!

Some useful applications include:

Drop and Undrop a Table

First let's see how we can restore data objects that have been accidentally or intentionally deleted.

In the CITIBIKE_ZERO_TO_SNOWFLAKE worksheet, run the following DROP command to remove the JSON_WEATHER_DATA table:

drop table json_weather_data;

Run a query on the table:

select * from json_weather_data limit 10;

In the results pane at the bottom, you should see an error because the underlying table has been dropped:

table dropped error

Now, restore the table:

undrop table json_weather_data;

The json_weather_data table should be restored. Verify by running the following query:

--verify table is undropped

select * from json_weather_data limit 10;

restored table result

Roll Back a Table

Let's roll back the TRIPS table in the CITIBIKE database to a previous state to fix an unintentional DML error that replaces all the station names in the table with the word "oops".

First, run the following SQL statements to switch your worksheet to the proper context:

use role sysadmin;

use warehouse compute_wh;

use database citibike;

use schema public;

Run the following command to replace all of the station names in the table with the word "oops":

update trips set start_station_name = 'oops';

Now, run a query that returns the top 20 stations by number of rides. Notice that the station names result contains only one row:

start_station_name as "station",
count(*) as "rides"
from trips
group by 1
order by 2 desc
limit 20;

one row result

Normally we would need to scramble and hope we have a backup lying around.

In Snowflake, we can simply run a command to find the query ID of the last UPDATE command and store it in a variable named $QUERY_ID.

set query_id =
(select query_id from table(information_schema.query_history_by_session (result_limit=>5))
where query_text like 'update%' order by start_time desc limit 1);

Use Time Travel to recreate the table with the correct station names:

create or replace table trips as
(select * from trips before (statement => $query_id));

Run the previous query again to verify that the station names have been restored:

start_station_name as "station",
count(*) as "rides"
from trips
group by 1
order by 2 desc
limit 20;

restored names result

In this section, we will explore aspects of Snowflake's access control security model, such as creating a role and granting it specific permissions. We will also explore other usage of the ACCOUNTADMIN (Account Administrator) role, which was briefly introduced earlier in the lab.

Continuing with the lab story, let's assume a junior DBA has joined Citi Bike and we want to create a new role for them with less privileges than the system-defined, default role of SYSADMIN.

Create a New Role and Add a User

In the CITIBIKE_ZERO_TO_SNOWFLAKE worksheet, switch to the ACCOUNTADMIN role to create a new role. ACCOUNTADMIN encapsulates the SYSADMIN and SECURITYADMIN system-defined roles. It is the top-level role in the account and should be granted only to a limited number of users.

In the CITIBIKE_ZERO_TO_SNOWFLAKE worksheet, run the following command:

use role accountadmin;

Notice that, in the top right of the worksheet, the context has changed to ACCOUNTADMIN:


Before a role can be used for access control, at least one user must be assigned to it. So let's create a new role named JUNIOR_DBA and assign it to your Snowflake user. To complete this task, you need to know your username, which is the name you used to log in to the UI.

Use the following commands to create the role and assign it to you. Before you run the GRANT ROLE command, replace YOUR_USERNAME_GOES_HERE with your username:

create role junior_dba;

grant role junior_dba to user YOUR_USERNAME_GOES_HERE;

Change your worksheet context to the new JUNIOR_DBA role:

use role junior_dba;

In the top right of the worksheet, notice that the context has changed to reflect the JUNIOR_DBA role.

JUNIOR_DBA context

Also, the warehouse is not selected because the newly created role does not have usage privileges on any warehouse. Let's fix it by switching back to ADMIN role and grant usage privileges to COMPUTE_WH warehouse.

use role accountadmin;

grant usage on warehouse compute_wh to role junior_dba;

Switch back to the JUNIOR_DBA role. You should be able to use COMPUTE_WH now.

use role junior_dba;

use warehouse compute_wh;

Finally, you can notice that in the database object browser panel on the left, the CITIBIKE and WEATHER databases no longer appear. This is because the JUNIOR_DBA role does not have privileges to access them.

Switch back to the ACCOUNTADMIN role and grant the JUNIOR_DBA the USAGE privilege required to view and use the CITIBIKE and WEATHER databases:

use role accountadmin;

grant usage on database citibike to role junior_dba;

grant usage on database weather to role junior_dba;

Switch to the JUNIOR_DBA role:

use role junior_dba;

Notice that the CITIBIKE and WEATHER databases now appear in the database object browser panel on the left. If they don't appear, try clicking ... in the panel, then clicking Refresh.

object browser panel with databases

View the Account Administrator UI

Let's change our access control role back to ACCOUNTADMIN to see other areas of the UI accessible only to this role. However, to perform this task, use the UI instead of the worksheet.

First, click the Home icon in the top left corner of the worksheet. Then, in the top left corner of the UI, click your name to display the user preferences menu. In the menu, go to Switch Role and select ACCOUNTADMIN.

switch UI role

Notice that once you switch the UI session to the ACCOUNTADMIN role, new tabs are available under Admin.


account usage

The Usage tab shows the following, each with their own page:

The filters in the top right corner of each page can be used to break down the usage/consumption/etc. visualizations by different measures.


account usage

The Security tab contains network policies created for the Snowflake account. New network policies can be created by selecting "+ Network Policy" at the top right hand side of the page.


account usage

The Billing tab contains the payment method for the account:

For the next section, stay in the ACCOUNTADMIN role for the UI session.

Snowflake enables data access between accounts through the secure data sharing features. Shares are created by data providers and imported by data consumers, either through their own Snowflake account or a provisioned Snowflake Reader account. The consumer can be an external entity or a different internal business unit that is required to have its own unique Snowflake account.

With secure data sharing:

Snowflake uses secure data sharing to provide account usage data and sample data sets to all Snowflake accounts. In this capacity, Snowflake acts as the data provider of the data and all other accounts.

Secure data sharing also powers the Snowflake Data Marketplace, which is available to all Snowflake customers and allows you to discover and access third-party datasets from numerous data providers and SaaS vendors. Again, in this data sharing model, the data doesn't leave the provider's account and you can use the datasets without any transformation.

View Existing Shares

In the home page, navigate to Data > Databases. In the list of databases, look at the SOURCE column. You should see two databases with Local in the column. These are the two databases we created previously in the lab. The other database, SNOWFLAKE, shows Share in the column, indicating it's shared from a provider.

arrow over database icon

Create an Outbound Share

Let's go back to the Citi Bike story and assume we are the Account Administrator for Snowflake at Citi Bike. We have a trusted partner who wants to analyze the data in our TRIPS database on a near real-time basis. This partner also has their own Snowflake account in the same region as our account. So let's use secure data sharing to allow them to access this information.

Navigate to Data > Private Sharing, then at the top of the tab click Shared by My Account. Click the Share button in the top right corner and select Create a Direct Share:

shares outbound button

Click + Select Data and navigate to the CITIBIKE database and PUBLIC schema. Select the 2 tables we created in the schema and click the Done button:

share fields

The default name of the share is a generic name with a random numeric value appended. Edit the default name to a more descriptive value that will help identify the share in the future (e.g. ZERO_TO_SNOWFLAKE_SHARED_DATA. You can also add a comment.

In a real-world scenario, the Citi Bike Account Administrator would next add one or more consumer accounts to the share, but we'll stop here for the purposes of this lab.

Click the Create Share button at the bottom of the dialog:

success message

The dialog closes and the page shows the secure share you created:


You can add consumers, add/change the description, and edit the objects in the share at any time. In the page, click the < button next to the share name to return to the Share with Other Accounts page:


We've demonstrated how it only takes seconds to give other accounts access to data in your Snowflake account in a secure manner with no copying or transferring of data required!

Snowflake provides several ways to securely share data without compromising confidentiality. In addition to tables, you can share secure views, secure UDFs (user-defined functions), and other secure objects. For more details about using these methods to share data while preventing access to sensitive information, see the Snowflake documentation.

Snowflake Data Marketplace

Make sure you're using the ACCOUNTADMIN role and, navigate to the Marketplace:

data marketplace tab

Find a listing

The search box at the top allows you to search for a listings. The drop-down lists to the right of the search box let you filter data listings by Provider, Business Needs, and Category.

Type COVID in the search box, scroll through the results, and select COVID-19 Epidemiological Data (provided by Starschema).

health tab

In the COVID-19 Epidemiological Data page, you can learn more about the dataset and see some usage example queries. When you're ready, click the Get button to make this information available within your Snowflake account:

get data fields

Review the information in the dialog and lick Get again:

get data fields

You can now click Done or choose to run the sample queries provided by Starschema:

get data fields

If you chose Open, a new worksheet opens in a new browser tab/window:

  1. Set your context
  2. Select the query you want to run (or place your cursor in the query text).
  3. Click the Run/Play button (or use the keyboard shortcut).
  4. You can view the data results in the bottom pane.
  5. When you are done running the sample queries, click the Home icon in the upper left corner.

get data fields


  1. Click Data > Databases
  2. Click the COVID19_BY_STARSCHEMA_DM database.
  3. You can see details about the schemas, tables, and views that are available to query.

covid19 databases

That's it! You have now successfully subscribed to the COVID-19 dataset from Starschema, which is updated daily with global COVID data. Notice we didn't have to create databases, tables, views, or an ETL process. We simply searched for and accessed shared data from the Snowflake Data Marketplace.

Positive To learn more about how to use the new worksheet interface, go to the Snowsight Docs

If you would like to reset your environment by deleting all the objects created as part of this lab, run the SQL statements in a worksheet.

First, ensure you are using the ACCOUNTADMIN role in the worksheet:

use role accountadmin;

Then, run the following SQL commands to drop all the objects we created in the lab:

drop share if exists zero_to_snowflake_shared_data;
-- If necessary, replace "zero_to_snowflake-shared_data" with the name you used for the share

drop database if exists citibike;

drop database if exists weather;

drop warehouse if exists analytics_wh;

drop role if exists junior_dba;

Congratulations on completing this introductory lab exercise! You've mastered the Snowflake basics and are ready to apply these fundamentals to your own data. Be sure to reference this guide if you ever need a refresher.

We encourage you to continue with your free trial by loading your own sample or production data and by using some of the more advanced capabilities of Snowflake not covered in this lab.

Additional Resources:

What we've covered: