The Zero to Snowflake Quickstart Guide — page 1

Snowflake for Developers

Guides

QUICKSTART

Zero to Snowflake

Dynamic Tables

Cameron Shimmin, Dureti Shemsi

FORK REPO

Overview

Who is Tasty Bytes?

Welcome to the Zero to Snowflake Quickstart! This guide is a consolidated journey through key areas of the Snowflake AI Data Cloud. You will start

with the fundamentals of warehousing and data transformation, build an automated data pipeline, then see how you can experiment with LLMs

using the Cortex Playground to compare different models for summarizing text, use AISQL Functions to instantly analyze customer review

sentiment with a simple SQL command, and harness Cortex Search for intelligent text discovery, and utilize Cortex Analyst for conversational

business intelligence. Finally, you will learn to secure your data with powerful governance controls and enrich your analysis through seamless data

collaboration.

We'll apply these concepts using a sample dataset from our fictitious food truck, Tasty Bytes, to improve and streamline their data operations. We'll

explore this dataset through several workload-specific scenarios, demonstrating the benefits Snowflake provides to businesses.

Expedition. Free, virtual, Nov 3–6.

Technical tracks for practitioners, outcomes for leaders.

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Prerequisites

What You Will Learn

What You Will Build

Setup

Overview

Step 1 - Create Your Setup SQL File

Step 2 - Add and Run the Setup Script

Our mission is to provide unique, high-quality food options in a convenient and cost-effective manner, emphasizing the use of fresh ingredients

from local vendors. Their vision is to become the largest food truck network in the world with a zero carbon footprint.

A Supported Snowflake

Browser

An Enterprise or Business Critical Snowflake Account

If you do not have a Snowflake Account, please

. When signing up, please make sure to select Enterprise

edition. You are welcome to choose any

Snowflake Cloud/Region

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

Vignette 1: Getting Started with Snowflake:

The fundamentals of Snowflake warehouses, caching, cloning, and Time Travel.

Vignette 2: Simple Data Pipelines:

How to ingest and transform semi-structured data using Dynamic Tables.

Vignette 3: Snowflake Cortex AI:

How to leverage Snowflake's comprehensive AI capabilities for experimentation, scalable analysis, AI-assisted

development, and conversational business intelligence.

Vignette 4: Governance with Horizon:

How to protect your data with roles, classification, masking, and row-access policies.

Vignette 5: Apps & Collaboration:

How to leverage the Snowflake Marketplace to enrich your internal data with third-party datasets.

A comprehensive understanding of the core Snowflake platform.

Configured Virtual Warehouses.

An automated ELT pipeline with Dynamic Tables.

A complete intelligence customer analytics platform leveraging Snowflake AI.

A robust data governance framework with roles and policies.

Enriched analytical views combining first- and third-party data.

In this guide, we will use

Snowflake Workspaces

to organize, edit, and run all the SQL scripts required for this course. We will create a dedicated

SQL file for the setup and each vignette. This will keep our code organized and easy to manage.

Let's walk through how to create your first SQL file, add the necessary setup code, and run it.

First, we need a place to put our setup script.

Navigate to

Workspaces

In the left-hand navigation menu of the Snowflake UI, click on

Projects

Workspaces

. This is the central hub for all

your SQL files.

Create a New SQL File:

Find and click the

+ Add New

button in the top-left corner of the

Workspaces

area, then select

SQL File

. This will

generate a new, blank SQL file.

Rename the SQL File:

Your new SQL file will have a name based on the timestamp it was created. Give it a descriptive name like

Zero To

Snowflake - Setup

Now that you have your SQL file, it's time to add the setup SQL and execute it.

Copy the SQL Code:

Click the link for the

setup file

and copy it to your clipboard.

Paste into your SQL File:

Return to your Zero To Snowflake Setup SQL file in Snowflake and paste the entire script into the editor.

START FOR FREE

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Looking Ahead

Get Started with Snowflake

Overview

What You Will Learn

Run the Script:

To execute all the commands in the SQL file sequentially, click the

"Run All"

button located at the top-left of the editor. This will

perform all the necessary setup actions, such as creating roles, schemas, and warehouses that you will need for the upcoming vignettes.

The process you just completed for creating a new SQL file is the exact same workflow you will use for every subsequent vignette in this course.

For each new vignette, you will:

1. Create a

new

SQL file.

2. Give it a descriptive name (e.g., Vignette 1 - Getting Started with Snowflake).

3. Copy and paste the SQL script for that specific vignette.

4. Each SQL file has all of the necessary instructions and commands to follow along.

Within this Vignette, we will learn about core Snowflake concepts by exploring Virtual Warehouses, using the query results cache, performing basic

data transformations, leveraging data recovery with Time Travel, and monitoring our account with Resource Monitors and Budgets.

How to create, configure, and scale a Virtual Warehouse.

How to leverage the Query Result Cache.

How to use Zero-Copy Cloning for development.

How to transform and clean data.

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What You Will Build

Get the SQL code and paste it into your SQL File.

Virtual Warehouses and Settings

Overview

Step 1 - Setting Context

Step 2 - Creating a Warehouse

Step 3 - Using and Resuming a Warehouse

How to instantly recover a dropped table using UNDROP.

How to create and apply a Resource Monitor.

How to create a Budget to monitor costs.

How to use Universal Search to find objects and information.

A Snowflake Virtual Warehouse

A development copy of a table using Zero-Copy Clone

A Resource Monitor

A Budget

Copy and paste the SQL code from this

file

in a new SQL File to follow along in Snowflake. Note that once you've reached the end of the SQL File

you can skip to Step 10 - Simple Data Pipeline

Virtual Warehouses are the dynamic, scalable, and cost-effective computing power that lets you perform analysis on your Snowflake data. Their

purpose is to handle all your data processing needs without you having to worry about the underlying technical details.

First, lets set our session context. To run the queries, highlight the three queries at the top of your SQL file and click the "

►

Run" button.

ALTER SESSION SET query_tag = '{"origin":"sf_sit-is","name":"tb_zts,"version":{"major":1, "minor":1},"attribut

USE DATABASE tb_101;

USE ROLE accountadmin;

Let's create our first warehouse! This command creates a new X-Small warehouse that will initially be suspended.

CREATE OR REPLACE WAREHOUSE my_wh

COMMENT = 'My TastyBytes warehouse'

WAREHOUSE_TYPE = 'standard'

WAREHOUSE_SIZE = 'xsmall'

MIN_CLUSTER_COUNT = 1

MAX_CLUSTER_COUNT = 2

SCALING_POLICY = 'standard'

AUTO_SUSPEND = 60

INITIALLY_SUSPENDED = true

AUTO_RESUME = false;

Virtual Warehouses

: A virtual warehouse, often referred to simply as a “warehouse”, is a cluster of compute resources in

Snowflake. Warehouses are required for queries, DML operations, and data loading. For more information, see the

Warehouse

Overview

Now that we have a warehouse, we must set it as the active warehouse for our session. Execute the next statement.

USE WAREHOUSE my_wh;

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Step 4 - Scaling a Warehouse

Query Result Cache

Overview

Step 1 - Re-running a Query

If you try to run the query below, it will fail, because the warehouse is suspended and does not have

AUTO_RESUME

enabled.

SELECT * FROM raw_pos.truck_details;

Let's resume it and set it to auto-resume in the future.

ALTER WAREHOUSE my_wh RESUME;

ALTER WAREHOUSE my_wh SET AUTO_RESUME = TRUE;

Now, try the query again. It should execute successfully.

SELECT * FROM raw_pos.truck_details;

Warehouses in Snowflake are designed for elasticity. We can scale our warehouse up on the fly to handle a more intensive workload. Let's scale our

warehouse to an X-Large.

ALTER WAREHOUSE my_wh SET warehouse_size = 'XLarge';

With our larger warehouse, let's run a query to calculate total sales per truck brand.

SELECT

o.truck_brand_name,

COUNT(DISTINCT o.order_id) AS order_count,

SUM(o.price) AS total_sales

FROM analytics.orders_v o

GROUP BY o.truck_brand_name

ORDER BY total_sales DESC;

This is a great place to demonstrate another powerful feature in Snowflake: the Query Result Cache. When you first ran the 'sales per truck' query,

it likely took several seconds. If you run the exact same query again, the result will be nearly instantaneous. This is because the query results were

cached in Snowflake's Query Result Cache.

Run the same 'sales per truck' query from the previous step. Note the execution time in the query details pane. It should be much faster.

SELECT

o.truck_brand_name,

COUNT(DISTINCT o.order_id) AS order_count,

SUM(o.price) AS total_sales

FROM analytics.orders_v o

GROUP BY o.truck_brand_name

ORDER BY total_sales DESC;

Query Result Cache

: Results are retained for any query for 24 hours. Hitting the result cache requires almost no compute

resources, making it ideal for frequently run reports or dashboards. The cache resides in the Cloud Services Layer, making it

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Step 2 - Scaling Down

Basic Transformation Techniques

Overview

Step 1 - Creating a Development Table with Zero-Copy Clone

Step 2 - Adding New Columns and Transforming Data

Step 3 - Cleaning the Data

globally accessible to all users and warehouses in the account. For more information, please visit the

documentation on using

persisted query results

We will now be working with smaller datasets, so we can scale our warehouse back down to an X-Small to conserve credits.

ALTER WAREHOUSE my_wh SET warehouse_size = 'XSmall';

In this section, we will see some basic transformation techniques to clean our data and use Zero-Copy Cloning to create development

environments. Our goal is to analyze the manufacturers of our food trucks, but this data is currently nested inside a

VARIANT

column.

First, let's take a look at the

truck_build

column.

SELECT truck_build FROM raw_pos.truck_details;

This table contains data about the make, model and year of each truck, but it is nested, or embedded in a special data type called a VARIANT. We

can perform operations on this column to extract these values, but first we'll create a development copy of the table.

Let's create a development copy of our

truck_details

table. Snowflake's Zero-Copy Cloning lets us create an identical, fully independent copy of

the table instantly, without using additional storage.

CREATE OR REPLACE TABLE raw_pos.truck_dev CLONE raw_pos.truck_details;

Zero-Copy Cloning

: Cloning creates a copy of a database object without duplicating the storage. Changes made to either the

original or the clone are stored as new micro-partitions, leaving the other object untouched.

Now that we have a safe development table, let's add columns for

year

make

, and

model

. Then, we will extract the data from the

truck_build

VARIANT

column and populate our new columns.

-- Add new columns

ALTER TABLE raw_pos.truck_dev ADD COLUMN IF NOT EXISTS year NUMBER;

ALTER TABLE raw_pos.truck_dev ADD COLUMN IF NOT EXISTS make VARCHAR(255);

ALTER TABLE raw_pos.truck_dev ADD COLUMN IF NOT EXISTS model VARCHAR(255);

-- Extract and update data

UPDATE raw_pos.truck_dev

SET

year = truck_build:year::NUMBER,

make = truck_build:make::VARCHAR,

model = truck_build:model::VARCHAR;

Let's run a query to see the distribution of truck makes.

SELECT

make,

COUNT(*) AS count

FROM raw_pos.truck_dev

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Step 4 - Promoting to Production with SWAP

Step 5 - Cleanup

Step 6 - Data Recovery with UNDROP

Step 7 - Verify the Drop

Step 8 - Restore the Table with UNDROP

Step 9 - Verify Restoration and Clean Up

GROUP BY make

ORDER BY make ASC;

Did you notice anything odd about the results from the last query? We can see a data quality issue: 'Ford' and 'Ford_' are being treated as separate

manufacturers. Let's easily fix this with a simple

UPDATE

statement.

UPDATE raw_pos.truck_dev

SET make = 'Ford'

WHERE make = 'Ford_';

Here we're saying we want to set the row's make value to

Ford

wherever it is

Ford_

. This will ensure none of the Ford makes have the underscore,

giving us a unified make count.

Our development table is now cleaned and correctly formatted. We can instantly promote it to be the new production table using the

SWAP WITH

command. This atomically swaps the two tables.

ALTER TABLE raw_pos.truck_details SWAP WITH raw_pos.truck_dev;

Now that the swap is complete, we can drop the unnecessary

truck_build

column from our new production table. We also need to drop the old

production table, which is now named

truck_dev

. But for the sake of the next lesson, we will "accidentally" drop the main table.

ALTER TABLE raw_pos.truck_details DROP COLUMN truck_build;

-- Accidentally drop the production table!

DROP TABLE raw_pos.truck_details;

Oh no! We accidentally dropped the production

truck_details

table. Luckily, Snowflake's Time Travel feature allows us to recover it instantly. The

UNDROP

command restores dropped objects.

If you run a

DESCRIBE

command on the table, you will get an error stating it does not exist.

DESCRIBE TABLE raw_pos.truck_details;

Let's restore the

truck_details

table to the exact state it was in before being dropped.

UNDROP TABLE raw_pos.truck_details;

Time Travel & UNDROP

: Snowflake Time Travel enables accessing historical data at any point within a defined period. This

allows for restoring data that has been modified or deleted.

UNDROP

is a feature of Time Travel that makes recovery from

accidental drops trivial.

Verify the table was successfully restored by selecting from it. Then, we can safely drop the actual development table,

truck_dev

-- Verify the table was restored

SELECT * from raw_pos.truck_details;

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Resource Monitors

Overview

Step 1 - Creating a Resource Monitor

Step 2 - Applying the Resource Monitor

Create a Budget

Overview

Step 1 - Creating a Budget via SQL

Step 2 - Budget Page in Snowsight

-- Now drop the real truck_dev table

DROP TABLE raw_pos.truck_dev;

Monitoring compute usage is critical. Snowflake provides Resource Monitors to track warehouse credit usage. You can define credit quotas and

trigger actions (like notifications or suspension) when thresholds are reached.

Let's create a resource monitor for

my_wh

. This monitor has a monthly quota of 100 credits and will send notifications at 75% and suspend the

warehouse at 90% and 100% of the quota. First, ensure your role is

accountadmin

USE ROLE accountadmin;

CREATE OR REPLACE RESOURCE MONITOR my_resource_monitor

WITH CREDIT_QUOTA = 100

FREQUENCY = MONTHLY

START_TIMESTAMP = IMMEDIATELY

TRIGGERS ON 75 PERCENT DO NOTIFY

ON 90 PERCENT DO SUSPEND

ON 100 PERCENT DO SUSPEND_IMMEDIATE;

With the monitor created, apply it to

my_wh

ALTER WAREHOUSE my_wh

SET RESOURCE_MONITOR = my_resource_monitor;

For more information on what each configuration handles, please visit the documentation for

Working with Resource

Monitors

While Resource Monitors track warehouse usage, Budgets provide a more flexible approach to managing all Snowflake costs. Budgets can track

spend on any Snowflake object and notify users when a dollar amount threshold is reached.

Let's first create the budget object in SQL.

CREATE OR REPLACE SNOWFLAKE.CORE.BUDGET my_budget()

COMMENT = 'My Tasty Bytes Budget';

Let's take a look at the Budget Page on Snowsight.

Navigate to

Admin

Cost Management

Budgets

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Step 3 - Configuring the Budget in Snowsight

Key:

1. Warehouse Context

2. Cost Management Navigation

3. Time Period Filter

4. Key Metrics Summary

5. Spend and Forecast Trend Chart

6. Budget Details

Configuring a budget is done through the Snowsight UI.

1. Make sure your account role is set to

ACCOUNTADMIN

. You can change this in the bottom left corner.

2. Click on the

MY_BUDGET

budget we created.

3. Click

Budget Details

to open the Budget details panel, then click

Edit

in the Budget Details panel on the right.

4. Set the

Spending Limit

100

5. Enter a verified notification email address.

6. Click

+ Tags & Resources

and add the

TB_101.ANALYTICS

schema and the

TB_DE_WH

warehouse to be monitored.

7. Click

Save Changes

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Universal Search

Overview

Step 1 - Searching for an Object

For a detailed guide on Budgets, please see the

Snowflake Budgets Documentation

Universal Search allows you to easily find any object in your account, plus explore data products in the Marketplace, relevant Snowflake

Documentation, and Community Knowledge Base articles.

Let's try it now.

1. Click

in the Navigation Menu on the left.

2. Enter

truck

into the search bar.

3. Observe the results. You will see categories of objects on your account, such as tables and views, as well as relevant documentation.

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Step 2 - Using Natural Language Search

You can also use natural language. For example, search for:

Which truck franchise has the most loyal customer base?

Universal search

will return relevant tables and views, even highlighting columns that might help answer your question, providing an excellent starting point for

analysis.

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Simple Data Pipeline

Overview

What You Will Learn

What You Will Build

Within this vignette, we will learn how to build a simple, automated data pipeline in Snowflake. We will start by ingesting raw, semi-structured data

from an external stage, and then use the power of Snowflake's Dynamic Tables to transform and enrich that data, creating a pipeline that

automatically stays up-to-date as new data arrives.

How to ingest data from an external S3 stage.

How to query and transform semi-structured VARIANT data.

How to use the FLATTEN function to parse arrays.

How to create and chain Dynamic Tables.

How an ELT pipeline automatically processes new data.

How to visualize a pipeline using the Directed Acyclic Graph (DAG).

An external Stage for data ingestion.

A staging table for raw data.

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Get the SQL and paste it into your SQL File.

External Stage Ingestion

Overview

Step 1 - Set Context

Step 2 - Create Stage and Staging Table

Step 3 - Copy Data into Staging Table

A multi-step data pipeline using three chained Dynamic Tables.

Copy and paste the SQL from this

file

in a new SQL File to follow along in Snowflake. Note that once you've reached the end of the SQL File you

can skip to Step 16 - Snowflake Cortex AI.

Our raw menu data currently sits in an Amazon S3 bucket as CSV files. To begin our pipeline, we first need to ingest this data into Snowflake. We

will do this by creating a Stage to point to the S3 bucket and then using the

COPY

command to load the data into a staging table.

First, let's set our session context to use the correct database, role, and warehouse. Execute the first few queries in your SQL file.

ALTER SESSION SET query_tag = '{"origin":"sf_sit-is","name":"tb_zts","version":{"major":1, "minor":1},"attribu

USE DATABASE tb_101;

USE ROLE tb_data_engineer;

USE WAREHOUSE tb_de_wh;

A Stage is a Snowflake object that specifies an external location where data files are stored. We'll create a stage that points to our public S3 bucket.

Then, we'll create the table that will hold this raw data.

-- Create the menu stage

CREATE OR REPLACE STAGE raw_pos.menu_stage

COMMENT = 'Stage for menu data'

URL = 's3://sfquickstarts/frostbyte_tastybytes/raw_pos/menu/'

FILE_FORMAT = public.csv_ff;

CREATE OR REPLACE TABLE raw_pos.menu_staging

(

menu_id NUMBER(19,0),

menu_type_id NUMBER(38,0),

menu_type VARCHAR(16777216),

truck_brand_name VARCHAR(16777216),

menu_item_id NUMBER(38,0),

menu_item_name VARCHAR(16777216),

item_category VARCHAR(16777216),

item_subcategory VARCHAR(16777216),

cost_of_goods_usd NUMBER(38,4),

sale_price_usd NUMBER(38,4),

menu_item_health_metrics_obj VARIANT

);

With the stage and table in place, let's load the data from the stage into our

menu_staging

table using the

COPY INTO

command.

COPY INTO raw_pos.menu_staging

FROM @raw_pos.menu_stage;

COPY INTO TABLE

: This powerful command loads data from a staged file into a Snowflake table. It is the primary method for

bulk data ingestion.

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Semi-Structured Data

Overview

Step 1 - Querying VARIANT Data

Step 2 - Parsing Arrays with FLATTEN

Dynamic Tables

Overview

Step 1 - Creating the First Dynamic Table

Snowflake excels at handling semi-structured data like JSON using its native

VARIANT

data type. One of the columns we ingested,

menu_item_health_metrics_obj

, contains JSON. Let's explore how to query it.

Let's look at the raw JSON. Notice it contains nested objects and arrays.

SELECT menu_item_health_metrics_obj FROM raw_pos.menu_staging;

We can use special syntax to navigate the JSON structure. The colon (

:

) accesses keys by name, and square brackets (

[]

) access array elements by

index. We can also cast results to explicit data types using the

CAST

function or the double-colon shorthand (

::

SELECT

menu_item_name,

CAST(menu_item_health_metrics_obj:menu_item_id AS INTEGER) AS menu_item_id, -- Casting using 'AS'

menu_item_health_metrics_obj:menu_item_health_metrics[0]:ingredients::ARRAY AS ingredients -- Casting usin

FROM raw_pos.menu_staging;

The

FLATTEN

function is a powerful tool for un-nesting arrays. It produces a new row for each element in an array. Let's use it to create a list of

every ingredient for every menu item.

SELECT

i.value::STRING AS ingredient_name,

m.menu_item_health_metrics_obj:menu_item_id::INTEGER AS menu_item_id

FROM

raw_pos.menu_staging m,

LATERAL FLATTEN(INPUT => m.menu_item_health_metrics_obj:menu_item_health_metrics[0]:ingredients::ARRAY) i;

Semi-Structured Data Types

: Snowflake's VARIANT, OBJECT, and ARRAY types allow you to store and query semi-structured

data directly, without needing to define a rigid schema upfront.

Our franchises are constantly adding new menu items. We need a way to process this new data automatically. For this, we can use Dynamic Tables,

a powerful tool designed to simplify data transformation pipelines by declaratively defining the result of a query and letting Snowflake handle the

refreshes.

We'll start by creating a dynamic table that extracts all unique ingredients from our staging table. We set a

LAG

of '1 minute', which tells Snowflake

the maximum amount of time this table's data can be behind the source data.

CREATE OR REPLACE DYNAMIC TABLE harmonized.ingredient

LAG = '1 minute'

WAREHOUSE = 'TB_DE_WH'

SELECT

ingredient_name,

menu_ids

FROM (

SELECT DISTINCT

i.value::STRING AS ingredient_name,

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Step 2 - Testing the Automatic Refresh

Build Out the Pipeline

Overview

Step 1 - Creating a Lookup Table

Step 2 - Adding Transactional Data

ARRAY_AGG(m.menu_item_id) AS menu_ids

FROM

raw_pos.menu_staging m,

LATERAL FLATTEN(INPUT => menu_item_health_metrics_obj:menu_item_health_metrics[0]:ingredients::ARRAY)

GROUP BY i.value::STRING

);

Dynamic Tables

: Dynamic Tables automatically refresh as their underlying source data changes, simplifying ELT pipelines and

ensuring data freshness without manual intervention or complex scheduling.

Let's see the automation in action. One of our trucks has added a Banh Mi sandwich, which contains new ingredients for French Baguette and

Pickled Daikon. Let's insert this new menu item into our staging table.

INSERT INTO raw_pos.menu_staging

SELECT

10101, 15, 'Sandwiches', 'Better Off Bread', 157, 'Banh Mi', 'Main', 'Cold Option', 9.0, 12.0,

PARSE_JSON('{"menu_item_health_metrics": [{"ingredients": ["French Baguette","Mayonnaise","Pickled Daikon"

Now, query the

harmonized.ingredient

table. Within a minute, you should see the new ingredients appear automatically.

-- You may need to wait up to 1 minute and re-run this query

SELECT * FROM harmonized.ingredient

WHERE ingredient_name IN ('French Baguette', 'Pickled Daikon');

Now we can build a multi-step pipeline by creating more dynamic tables that read from other dynamic tables. This creates a chain, or a Directed

Acyclic Graph (DAG), where updates automatically flow from the source to the final output.

Let's create a lookup table that maps ingredients to the menu items they are used in. This dynamic table reads from our

harmonized.ingredient

dynamic table.

CREATE OR REPLACE DYNAMIC TABLE harmonized.ingredient_to_menu_lookup

LAG = '1 minute'

WAREHOUSE = 'TB_DE_WH'

SELECT

i.ingredient_name,

m.menu_item_health_metrics_obj:menu_item_id::INTEGER AS menu_item_id

FROM

raw_pos.menu_staging m,

LATERAL FLATTEN(INPUT => m.menu_item_health_metrics_obj:menu_item_health_metrics[0]:ingredients) f

JOIN harmonized.ingredient i ON f.value::STRING = i.ingredient_name;

Let's simulate an order of two Banh Mi sandwiches by inserting records into our order tables.

INSERT INTO raw_pos.order_header

SELECT

459520441, 15, 1030, 101565, null, 200322900,

TO_TIMESTAMP_NTZ('08:00:00', 'hh:mi:ss'),

TO_TIMESTAMP_NTZ('14:00:00', 'hh:mi:ss'),

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Step 3 - Creating the Final Pipeline Table

Step 4 - Querying the Final Output

Visualize the Pipeline

Overview

null, TO_TIMESTAMP_NTZ('2022-01-27 08:21:08.000'),

null, 'USD', 14.00, null, null, 14.00;

INSERT INTO raw_pos.order_detail

SELECT

904745311, 459520441, 157, null, 0, 2, 14.00, 28.00, null;

Finally, let's create our final dynamic table. This one joins our order data with our ingredient lookup tables to create a summary of monthly

ingredient usage per truck. This table depends on the other dynamic tables, completing our pipeline.

CREATE OR REPLACE DYNAMIC TABLE harmonized.ingredient_usage_by_truck

LAG = '2 minute'

WAREHOUSE = 'TB_DE_WH'

SELECT

oh.truck_id,

EXTRACT(YEAR FROM oh.order_ts) AS order_year,

MONTH(oh.order_ts) AS order_month,

i.ingredient_name,

SUM(od.quantity) AS total_ingredients_used

FROM

raw_pos.order_detail od

JOIN raw_pos.order_header oh ON od.order_id = oh.order_id

JOIN harmonized.ingredient_to_menu_lookup iml ON od.menu_item_id = iml.menu_item_id

JOIN harmonized.ingredient i ON iml.ingredient_name = i.ingredient_name

JOIN raw_pos.location l ON l.location_id = oh.location_id

WHERE l.country = 'United States'

GROUP BY

oh.truck_id,

order_year,

order_month,

i.ingredient_name

ORDER BY

oh.truck_id,

total_ingredients_used DESC;

Now, let's query the final table in our pipeline. After a few minutes for the refreshes to complete, you will see the ingredient usage for two Banh

Mis from the order we inserted in a previous step. The entire pipeline updated automatically.

-- You may need to wait up to 2 minutes and re-run this query

SELECT

truck_id,

ingredient_name,

SUM(total_ingredients_used) AS total_ingredients_used

FROM

harmonized.ingredient_usage_by_truck

WHERE

order_month = 1

AND truck_id = 15

GROUP BY truck_id, ingredient_name

ORDER BY total_ingredients_used DESC;

Finally, let's visualize our pipeline's Directed Acyclic Graph, or DAG. The DAG shows how our data flows through the tables, and it can be used to

monitor the health and lag of our pipeline.

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Step 1 - Accessing the Graph View

Snowflake Cortex AI

Overview

What You Will Learn

To access the DAG in Snowsight:

1. Navigate to

Data

Database

2. In the database object explorer, expand your database

TB_101

and the schema

HARMONIZED

3. Click on

Dynamic Tables

4. Select any of the dynamic tables you created (e.g.,

INGREDIENT_USAGE_BY_TRUCK

5. Click on the

Graph

tab in the main window.

You will now see a visualization of your pipeline, showing how the base tables flow into your dynamic tables.

Welcome to the Zero to Snowflake guide focused on Snowflake Cortex AI!

Within this guide, we will explore Snowflake's complete AI platform through a progressive journey from experimentation into unified business

intelligence. We'll learn AI capabilities by building a comprehensive customer intelligence system using Cortex Playground for AI experimentation,

Cortex AI Functions for production-scale analysis, Cortex Search for semantic text searching, and Cortex Analyst for natural language analytics.

For more detail on Snowflake Cortex AI, please visit the

Snowflake AI and ML Overview documentation

How to Experiment with AI Using AI Cortex Playground for model testing and prompt optimization.

How to Scale AI Analysis with Cortex AI Functions for production-scale customer review processing.

How to enable semantic discovery with Cortex Search for intelligent text and review finding.

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What You Will Build

Cortex Playground

Overview

Step 1 - Connect Data & Filter

How to create conversational analytics with Cortex Analyst for natural language business intelligence.

Through this journey, you’ll construct a complete intelligence customer analytics platform:

Phase 1: AI Foundation

AI Experimentation Environment using Cortex Playground for model testing and optimization.

Production-scale Review Analysis pipeline using Cortex AI Functions for systematic customer feedback processing.

Phase 2: Intelligent Development & Discovery

Semantic Search Engine using Cortex Search for instant customer feedback discovery and operational intelligence.

Phase 3: Conversational Intelligence

Natural Language Business Analytics Interface using Cortex Analyst for conversational data exploration.

Unified AI Business Intelligence Platform using Snowflake CoWork that connects customer voice with business performance

As a data analyst at Tasty Bytes, you need to rapidly explore customer feedback using AI models to identify service improvement opportunities.

Traditionally, AI experimentation is complex and time-consuming.

Snowflake Cortex Playground

solves this by offering a quick, secure environment

directly within Snowflake's UI to experiment with diverse AI models, compare their performance on real business data, and export successful

approaches as production-ready SQL. This module guides you through using Cortex Playground for rapid prototyping and seamless integration of AI

into your data workflows.

Let's begin by connecting directly to customer review data within Cortex Playground. This keeps your data secure within Snowflake while allowing

you to analyze feedback using AI models.

Navigation steps:

1. Navigate to

AI & ML → Studio → Cortex Playground

2. Select

Role: TB_DEV

and

Warehouse: TB_DEV_WH

3. Click "

+Connect your data

" in the prompt box.

4. Select data source:

5. Click

Let's go

6. Select text column:

REVIEW

7. Select filter column:

TRUCK_BRAND_NAME

8. Click

Done

9. In the system prompt box, apply a filter using the

TRUCK_BRAND_NAME

dropdown. There are multiple reviews available for each truck brand.

For instance, you can select "

Better Of Bread

" to narrow down the reviews. If "

Better Of Bread

" isn't available, please choose any other truck

brand from the dropdown and proceed with one of its reviews.

Database: TB_101

Schema: HARMONIZED

Table: TRUCK_REVIEWS_V

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Step 2 - Compare AI Models for Insights

What you've accomplished:

You now have direct access to customer review data within the AI interface. The filter allows you

to focus your analysis on specific truck brands, making your experiment more targeted and relevant.

Now, let's analyze customer reviews to extract specific operational insights and compare how different AI models perform on this business task.

Setup Model Comparison:

1. Click "

Compare

" to enable side-by-side model comparison.

2. Set the left panel to "

claude-3-5-sonnet

" and the right panel to "

snowflake-llama-3.3-70b

Note:

Snowflake Cortex provides access to leading AI models from multiple providers, including Anthropic, OpenAI, Meta, and

others, giving you choice and flexibility without vendor lock-in.

Enter this strategic prompt:

Analyze this customer review across multiple dimensions: sentiment score with confidence level, key theme extraction,

competitive positioning insights, operational impact assessment, and priority ranking for management action

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Step 3 - Fine-Tune Model Behavior

Key Insight:

Notice the distinct strengths: Claude provides structured, executive-ready analysis with clear confidence. In

contrast, Snowflake’s Llama model, optimized specifically for robust business intelligence, delivers comprehensive operational

intelligence enriched with strategic context and detailed competitive analysis. This highlights the power of leveraging multiple

AI providers, empowering you to choose the ideal approach for your specific business needs.

With our optimal model identified, we now need to fine-tune its behavior for different business scenarios. The same model can produce vastly

different results depending on its settings—let’s optimize this for our specific analytical requirements.

We want to observe how adjusting parameters, especially "

temperature

," affects the AI model's responses. Does it lead to more consistent or more

creative answers?

How to Set Up This Temperature Test:

1. First, make sure both panels are set to "

claude-3-5-sonnet

." We're comparing the same model, just with different settings.

2. Next, click "

Change Settings

" right next to where it says "

Compare

3. Now, let's adjust those parameters for each side:

4. Finally, use the exact same strategic prompt you used in Step 2.

Left Panel:

Set

Temperature

0.1

. This will generally make the model give you really consistent, predictable answers.

Set

Max-tokens

200

. This just keeps the responses from getting too long.

Right Panel:

Set

Temperature

0.8

. This should make the model's answers a bit more creative and varied.

Set

top_p

0.8

. This is another setting that helps encourage a wider range of words in the response.

Set

Max-tokens

200

. Again, keeping the length in check.

Give that a try and see how the responses differ! It's pretty cool to see how these small tweaks can change the AI's "personality."

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Step 4 - Understanding the Underlying Technology

The Foundation: SQL at Its Core

Observe the Impact:

Notice how adjusting the temperature parameter fundamentally changes the analytical output, even with the same AI model and data.

Temperature 0.1:

Produces deterministic, focused output. Ideal for structured, consistent analysis and standardized reporting.

Temperature 0.8:

Results in diverse, varied output. Perfect for generating explanatory insights or exploring less obvious connections.

While temperature influences token choice,

top_p

(set to 0.8 on the right) restricts possible tokens.

max_tokens

simply sets the maximum response

length; be mindful small values can truncate results. This gives you precise control over AI creativity versus consistency, letting you match the AI’s

behavior to your analytical objectives.

Now that we’ve mastered model selection and parameter optimization, let's examine the technology foundation that makes this experimentation

possible. Understanding this will help us transition from playground testing to production deployment.

In this section, let's explore the core technology that takes your AI insights from the playground to production.

Every AI insight you generate in Cortex Playground isn't just magic; it's backed by SQL. Click "

View Code

" after any model response, and you'll see

the exact SQL query, complete with your specified settings like temperature. This isn't just for show—this code is ready for action! You can run it

directly in a Workspace SQL file, automate it with streams and tasks, or integrate it with a dynamic table for live data processing. It's also worth

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The AI_COMPLETE Function

Conclusion

AI Functions

Overview

Get the SQL code and paste it into your SQL File.

noting that the functionalities of this Cortex Complete can be accessed programmatically via Python or a REST API, offering flexible integration

options.

Behind every prompt you've run, the

AI_COMPLETE

function is hard at work. This is Snowflake Cortex AI's powerful function providing direct

access to industry-leading large language models for text completion. The Cortex Playground simply offers an intuitive interface to test and

compare these models before you embed them directly into your SQL. (Heads up: AI_COMPLETE is the updated version of

SNOWFLAKE.CORTEX.COMPLETE)

This seamless integration means your AI experimentation directly translates into production-ready workflows within Snowflake.

The Cortex Playground is an invaluable tool for experimenting with individual reviews, but true large-scale customer feedback analysis demands

specialized AI functions. The prompt patterns and model selections you've refined here lay the groundwork for building scalable solutions. Our next

step involves processing thousands of reviews using purpose-built AI Functions like

SENTIMENT()

CLASSIFY()

EXTRACT_ANSWER()

, and

AI_SUMMARIZE_AGG()

. This systematic approach ensures that AI-driven insights seamlessly become a core part of our operational strategy.

You've successfully experimented with AI models in Cortex Playground to analyze individual customer reviews. Now, it's time to scale! This guide

shows you how to use

AI Functions

to process thousands of reviews, turning experimental insights into production-ready intelligence. You'll learn

to:

USE SENTIMENT()

to score and label truck customer reviews.

Use AI_CLASSIFY()

to categorize reviews by themes.

Use EXTRACT_ANSWER()

to pull specific complaints or praise.

Use AI_SUMMARIZE_AGG()

to generate quick summaries per truck brand.

Copy and paste the SQL from this

file

in a new SQL File to follow along in Snowflake.

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Step 1 - Setting Context

Step 2 - Sentiment Analysis at Scale

First, let's set our session context. We will assume the role of a TastyBytes data analyst with the intention of leveraging AISQL functions to gain

insights from customer reviews.

ALTER SESSION SET query_tag = '{"origin":"sf_sit-is","name":"tb_zts","version":{"major":1, "minor":1},"attribu

USE ROLE tb_analyst;

USE DATABASE tb_101;

USE WAREHOUSE tb_de_wh;

Analyze customer sentiment across all food truck brands to identify which trucks are performing best and create fleet-wide customer satisfaction

metrics. In Cortex Playground, we analyzed individual reviews manually. Now we’ll use the

SENTIMENT()

function to automatically score customer

reviews from -1 (negative) to +1 (positive), following Snowflake's official sentiment ranges.

Business Question:

“How do customers feel about each of our truck brands overall?”

Please execute this query to analyze customer sentiment across our food truck network and categorize feedback.

SELECT

truck_brand_name,

COUNT(*) AS total_reviews,

AVG(CASE WHEN sentiment >= 0.5 THEN sentiment END) AS avg_positive_score,

AVG(CASE WHEN sentiment BETWEEN -0.5 AND 0.5 THEN sentiment END) AS avg_neutral_score,

AVG(CASE WHEN sentiment <= -0.5 THEN sentiment END) AS avg_negative_score

FROM (

SELECT

truck_brand_name,

SNOWFLAKE.CORTEX.SENTIMENT (review) AS sentiment

FROM harmonized.truck_reviews_v

WHERE

language ILIKE '%en%'

AND review IS NOT NULL

LIMIT 10000

)

GROUP BY

truck_brand_name

ORDER BY total_reviews DESC;

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Step 3 - Categorize Customer Feedback

Key Insight

: Notice how we transitioned from analyzing reviews one at a time in Cortex Playground to systematically

processing thousands. The

SENTIMENT()

function automatically scored every review and categorized them into Positive,

Negative, and Neutral - giving us instant fleet-wide customer satisfaction metrics.

Sentiment Score Ranges

Positive: 0.5 to 1

Neutral: -0.5 to 0.5

Negative: -0.5 to -1

Now, let's categorize all reviews to understand what aspects of our service customers are talking about most. We'll use the

AI_CLASSIFY()

function, which automatically categorizes reviews into user-defined categories based on AI understanding, rather than simple keyword matching. In

this step, we will categorize customer feedback into business-relevant operational areas and analyze their distribution patterns.

Business Question:

“What are customers primarily commenting on - food quality, service, or delivery experience?"

Execute the Classification Query:

WITH classified_reviews AS (

SELECT

truck_brand_name,

AI_CLASSIFY(

review,

['Food Quality', 'Pricing', 'Service Experience', 'Staff Behavior']

):labels[0] AS feedback_category

FROM

harmonized.truck_reviews_v

WHERE

language ILIKE '%en%'

AND review IS NOT NULL

AND LENGTH(review) > 30

LIMIT

10000

)

SELECT

truck_brand_name,

feedback_category,

COUNT(*) AS number_of_reviews

FROM

classified_reviews

GROUP BY

truck_brand_name,

feedback_category

ORDER BY

truck_brand_name,

number_of_reviews DESC;

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Step 4 - Extract Specific Insights

Key Insight

: Observe how

AI_CLASSIFY()

automatically categorized thousands of reviews into business-relevant themes

such as Food Quality, Service Experience, and more. We can instantly see that Food Quality is the most discussed topic across

our truck brands, providing the operations team with clear, actionable insight into customer priorities.

Next, to gain precise answers from unstructured text, we'll utilize the

EXTRACT_ANSWER()

function. This powerful function enables us to ask

specific business questions about customer feedback and receive direct answers. In this step, our goal is to identify precise operational issues

mentioned in customer reviews, highlighting specific problems that require immediate attention.

Business question:

“What specific improvement or complaint is mentioned in this review?"

Let's execute the next query:

SELECT

truck_brand_name,

primary_city,

LEFT(review, 100) || '...' AS review_preview,

SNOWFLAKE.CORTEX.EXTRACT_ANSWER(

review,

'What specific improvement or complaint is mentioned in this review?'

) AS specific_feedback

FROM

harmonized.truck_reviews_v

WHERE

language = 'en'

AND review IS NOT NULL

AND LENGTH(review) > 50

ORDER BY truck_brand_name, primary_city ASC

LIMIT 10000;

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Step 5 - Generate Executive Summaries

Key Insight

: Notice how

EXTRACT_ANSWER()

distills specific, actionable insights from long customer reviews. Rather than

manual review, this function automatically identifies concrete feedback like "friendly staff was saving grace" and "hot dogs are

cooked to perfection." The result is a transformation of dense text into specific, quotable feedback that the operations team

can leverage instantly.

Finally, to create concise summaries of customer feedback, we'll use the

AI_SUMMARIZE_AGG()

function. This powerful function generates short,

coherent summaries from lengthy unstructured text. In this step, our goal is to distill the essence of customer reviews for each truck brand into

digestible summaries, providing quick overviews of overall sentiment and key points.

Business Question:

“What are the key themes and overall sentiment for each truck brand?”

Execute the Summarization Query:

SELECT

truck_brand_name,

AI_SUMMARIZE_AGG (review) AS review_summary

FROM

(

SELECT

truck_brand_name,

review

FROM

harmonized.truck_reviews_v

LIMIT

100

)

GROUP BY

truck_brand_name;

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Conclusion

Optional: Cortex Search

Overview

Step 1 - Access Cortex Search

Key Insight

: The

AI_SUMMARIZE_AGG()

function condenses lengthy reviews into clear, brand-level summaries. These

summaries highlight recurring themes and sentiment trends, providing decision-makers with quick overviews of each food

truck's performance and enabling faster understanding of customer perception without reading individual reviews.

We've successfully demonstrated the transformative power of AI Functions, shifting customer feedback analysis from individual review processing

to systemic, production-scale intelligence. Our journey through these four core functions clearly illustrates how each serves a distinct analytical

purpose, transforming raw customer voices into comprehensive business intelligence—systematic, scalable, and immediately actionable. What once

required individual review analysis now processes thousands of reviews in seconds, providing both the emotional context and specific details

crucial for data-driven operational improvements.

While AI-powered tools excel at generating complex analytical queries, a common daily challenge for customer service teams is quickly finding

specific customer reviews for complaints or compliments. Traditional keyword search often falls short, missing the nuances of natural language.

Snowflake Cortex Search

solves this by providing low-latency, high-quality "fuzzy" search over your Snowflake text data. It quickly sets up hybrid

(vector and keyword) search engines, handling embeddings, infrastructure, and tuning for you. Under the hood, Cortex Search combines semantic

(meaning-based) and lexical (keyword-based) retrieval with intelligent re-ranking to deliver the most relevant results. In this lab, you will configure a

search service, connect it to customer review data, and run semantic queries to proactively identify key customer feedback.

1. Open Snowsight and navigate to the AI & ML Studio, then select

Cortex Search

2. Click

Create

to begin setup.

This opens the search service configuration interface, where you’ll define how Snowflake indexes and interprets your text data.

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Step 2 - Configure the Search Service

Step 3 - Connect to Review Data

In the

New service

configuration screen:

1. Select

Database

and

Schema

2. Enter the

Service name

customer_feedback_intelligence

3. Click the

button at the bottom right to proceed.

Choose

TB_101

from the Databases dropdown

Choose

HARMONIZED

from the Schemas dropdown

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The wizard will now guide you through several configuration screens. Follow these steps:

Select data screen:

From the Views dropdown, select

TRUCK_REVIEWS_V

Click

Select search column screen:

Choose

REVIEW

(this is the text column that will be semantically searched)

Click

Select attributes screen:

Select columns for filtering results:

TRUCK_BRAND_NAME

PRIMARY_CITY

REVIEW_ID

Click

Select columns screen:

Choose other columns to include in search results such as

DATE

LANGUAGE

, etc.

Click

Configure indexing screen:

Warehouse

: Select

COMPUTE_WH

from the dropdown

Accept the other default settings

Click

Create

to build the search service

Note

: Creating the search service includes building the index, so the initial setup may take a little longer. If the creation

process is taking an extended period, you can seamlessly continue the lab by using a pre-configured search service:

1. From the left-hand menu in Snowsight, navigate to

AI & ML

, then click on

Cortex Search

2. In the Cortex Search view, locate the dropdown filter (as highlighted in the image below, showing

TB_101 / HARMONIZED

). Select or ensure this

filter is set to

TB_101 / HARMONIZED

3. In the list of "Search services" that appears, click on the pre-built service named

TASTY_BYTES_REVIEW_SEARCH

4. Once inside the service's details page, click on

Playground

in the top right corner to begin using the search service for the lab.

Once any search service is active (either your new one or the pre-configured one), queries will run with low latency and scale seamlessly.

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Step 4 - Run Semantic Query

Behind this simple UI, Cortex Search is performing a complex task. It analyzes the text in your "REVIEW" column, using an AI

model to generate semantic embeddings, which are numerical representations of the text's meaning. These embeddings are

then indexed, allowing for high-speed conceptual searches later on. In just a few clicks, you have taught Snowflake to

understand the intent behind your reviews.

When the service shows as "Active", click on

Playground

and enter the natural language prompt in the search bar:

Prompt - 1:

Customers getting sick

Key Insight

: Notice Cortex Search isn’t just finding customers - it’s finding CONDITIONS that could MAKE customers sick.

That is the difference between reactive keyword search and proactive semantic understanding.

Now try another query:

Prompt - 2:

Angry customers

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Conclusion

Optional: Cortex Analyst

Overview

Step 1 - Design Semantic Model

Key Insight

: These customers are about to churn, but they never said “I’m angry.” They expressed frustration in their own

words. Cortex Search understands the emotion behind the language, helping you identify and save at-risk customers before

they leave.

Ultimately, Cortex Search transforms how Tasty Bytes analyzes customer feedback. It empowers the customer service manager to move beyond

simply sifting through reviews, to truly understand and proactively act upon the voice of the customer at scale, driving better operational decisions

and enhancing customer loyalty.

In the next module - Cortex Analyst - you'll use natural language to query structured data.

A business analyst at Tasty Bytes needs to enable self-service analytics, allowing the business team to ask complex questions in natural language

and get instant insights without relying on data analysts to write SQL. While previous AI tools helped with finding reviews and complex query

generation, the demand now is for

conversational analytics

that directly transforms structured business data into immediate insights.

Snowflake Cortex Analyst

empowers business users to ask sophisticated questions directly, seamlessly extracting value from their analytics data

through natural language interaction. This lab will guide you through designing a semantic model, connecting it to your business data, configuring

relationships and synonyms, and then executing advanced business intelligence queries using natural language.

Let's begin by navigating to Cortex Analyst in Snowsight and configuring our semantic model foundations.

1. Navigate to

Cortex Analyst

under

AI & ML Studio

in Snowsight.

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Set Role and Warehouse:

Change role to

TB_DEV

Set Warehouse to

TB_CORTEX_WH

Click

Create new model

3. On the

Getting Started

page, configure the following:

DATABASE

TB_101

SCHEMA

SEMANTIC_LAYER

Name

tasty_bytes_business_analytics

Description

Semantic model for Tasty Bytes executive analytics, covering customer loyalty and order performance

data for natural language querying

Click

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Step 2 - Select & Configure Tables and Columns

Step 3 - Edit Logical Table & Add Synonyms

Step 4 - Configure Table Relationships

In the

Select tables

step, let's choose our analytics views.

Select the core business tables:

DATABASE

TB_101

SCHEMA

SEMANTIC_LAYER

VIEWS

: Select

Customer_Loyalty_Metrics_v

and

Orders_v

Click

On the

Select columns

page, ensure both selected tables are active, then click

Create and Save

Now, let's add table synonyms and a primary key for better natural language understanding.

In the

customer_loyalty_metrics_v

table, copy and paste the following synonyms into the

Synonyms

box:

Customers, customer_data, loyalty, customer_metrics, customer_info

Set the

Primary Key

customer_id

from the dropdown.

For the

orders_v

table, copy and paste the following synonyms:

Orders, transactions, sales, purchases, order_data

After making these changes, click

Save

in the top right corner.

After creating the semantic model, let's establish the relationship between our logical tables.

Click

Relationships

in the left-hand navigation.

Click

Add relationship

Configure the relationship as follows:

Relationship name

orders_to_customer_loyalty_metrics

Left table

ORDERS_V

Right table

CUSTOMER_LOYALTY_METRICS_V

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Step 5 - Execute Customer Segmentation Intelligence

Join columns

: Set

CUSTOMER_ID

CUSTOMER_ID

Click

Add relationship

Upon completion

, simply use the

Save

option at the top of the UI. This will finalize your semantic view, making your semantic model ready for

sophisticated natural language queries.

To access the

Cortex Analyst chat interface

in fullscreen mode, you would:

1. Click the

three-dot menu (ellipsis)

next to the "Share" button at the top right.

2. From the dropdown menu, select

"Enter fullscreen mode."

With our semantic model and relationships active, let's demonstrate sophisticated natural language analysis by running our first complex business

query.

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Step 6 - Generate Advanced Business Intelligence

Navigate to the Cortex Analyst query interface.

Enter the following prompt:

Show customer groups by marital status and gender, with their total spending per customer and average order

Key Insight

: Instantly delivers comprehensive intelligence by combining multi-table joins, demographic segmentation,

geographic insights, and lifetime value analysis - insights that would require 40+ lines of SQL and hours of analyst effort.

Having seen basic segmentation, let's now demonstrate enterprise-grade SQL that showcases the full power of conversational business

intelligence.

Clear the context by clicking the refresh icon.

Enter the following prompt:

I want to understand our customer base better. Can you group customers by their total spending (high, medium

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Conclusion

Snowflake CoWork

Overview

Key Insight

: Notice how Cortex Analyst seamlessly bridges the gap between a business user's simple, natural language

question and the sophisticated, multi-faceted SQL query required to answer it. It automatically constructs the complex logic,

including CTEs, window functions, and detailed aggregations, that would typically demand a skilled data analyst.

Through these rigorous steps, we've forged a robust Cortex Analyst semantic model. This isn't just an improvement; it's a transformative tool

designed to liberate users across various industries from the constraints of SQL, enabling them to surface profound business intelligence through

intuitive natural language queries. Our multi-layered analyses, while showcased through the Tasty Bytes use case, powerfully illustrate how this

model drastically cuts down on the time and effort traditionally needed for deep insights, thereby democratizing access to data and fueling a

culture of informed, agile decision-making on a broad scale.

The Chief Operating Officer at Tasty Bytes receives dozens of fragmented reports each week: customer satisfaction dashboards, revenue analytics,

operational performance metrics, and market analysis. Critical business insights remain buried across separate systems: customer sentiment lives in

review platforms, sales data sits in financial dashboards, and operational metrics exist in isolated performance tools.

When the COO needs to understand why Q3 revenue dropped, connecting customer feedback sentiment with actual financial performance

requires hours of manual analysis, SQL expertise, and cross-referencing multiple data sources. This is a significant hurdle for executives and other

non-technical roles.

In this section, we'll demonstrate how

Snowflake CoWork

tackles this challenge by combining the capabilities of Cortex Search and Cortex Analyst,

which are made available through the setup. This integration allows for a single conversational AI agent. You'll see how executives and other non-

technical roles can ask natural language questions and receive immediate answers with visualizations. This kind of insight would normally take

weeks of analyst work across multiple teams.

Prerequisites:

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Step 1 - Upload Semantic Model

Step 2: Create a Unified Agent

Before starting this module, your environment includes pre-configured AI services that power Snowflake CoWork:

Cortex Search Service:

tasty_bytes_review_search

- analyzing customer reviews and feedback

Note for Advanced Users: If you want to build your own Cortex Search from scratch, an optional setup module is available. For a detailed

guide, click the link to the:

Cortex Search Module

Cortex Analyst Service:

TASTY_BYTES_BUSINESS_ANALYTICS

- for translating natural language questions into SQL and providing insights from

structured data, enabling self-service analytics.

Note for Advanced Users: If you prefer to build your own Cortex Analyst semantic model from scratch, you can access the detailed setup

module for guidance. Access the detailed setup by clicking on the:

Cortex Analyst Module

To enable business analytics capabilities in Snowflake CoWork, you need to upload the pre-built semantic model file to your Snowflake stage. You

can

download the necessary YAML file directly by clicking this link:

Cortex Analyst Semantic Model

Important:

If clicking the link opens the file in your browser instead of downloading it, please right-click on the link and select

"Save Link As"

download the YAML file to your local machine.

Here's how to upload the semantic model:

Navigate to Cortex Analyst

: In Snowsight, go to

AI & ML Studio

and then select

Cortex Analyst

Set Role and Warehouse

Change your role to

TB_DEV

Set the warehouse to

TB_CORTEX_WH

Upload your YAML file

: Click the

Upload your yaml file

button.

Configure Upload Details

: In the upload file screen, set the following:

Database

Tb_101

Schema

semantic_layer

Stage

semantic_model_stage

Click Upload

: This YAML file contains the pre-configured semantic model that defines the business analytics layer, including customer loyalty

metrics and order data.

Save the YAML file

: After clicking upload, save the YAML file. The semantic model will then appear in the Cortex Analyst panel, in the semantic

models section.

With your AI services pre-configured, you can now create a Cortex Agent that combines these capabilities into a single, unified intelligence

interface.

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Create the Agent

Configure the Agent

1. In

Snowsight

, navigate to the

AI & ML Studio

, then select

Agents

2. Click

Create Agent

3. In the "Create New Agent" window, click

Create agent

Initial Configuration

5. Click

Create agent

Platform integration

: Ensure "Create this agent for Snowflake CoWork" is checked.

Database and schema

: This will default to

SNOWFLAKE_INTELLIGENCE.AGENTS

Agent object name

: Enter

tasty_bytes_intelligence_agent

Display name

: Enter

Tasty Bytes Business Intelligence Agent

After creating the agent, click on its name from the agent list to open the details page, then click

Edit

to begin configuring it.

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1. About Tab

2. Tools Tab

Display name

Tasty Bytes Business Intelligence Agent

Description

This agent analyzes customer feedback and business performance data for Tasty Bytes food trucks. It identifies

Note

: This lab primarily uses a pre-built

semantic model

uploaded in Step 1. However, if you built your Cortex Analyst

semantic view from scratch using the

Cortex Analyst Module

, you will select your

semantic view

here instead of a semantic

model. After setting the

Database

TB_101

and

Schema

semantic_layer

, your semantic view will be listed and

selectable under that schema.

Now, let's add the semantic model we uploaded in Step 1:

Add the Cortex Analyst Tool:

1. Click

Add

next to "Cortex Analyst."

2. Select the

Semantic model file

radio button.

Configure the semantic model location

Configure tool details

Schema

: Choose

TB_101.SEMANTIC_LAYER

Stage

: Choose

SEMANTIC_MODEL_STAGE

File Selection

: Pick your uploaded YAML file from the list.

Name

: Enter

tasty_bytes_business_analytics

Description

Searches customer reviews and feedback to identify sentiment, operational issues, and customer satisfaction in

Configure execution settings

6. Click

Add

Warehouse

: Select

Custom

and choose

TB_CORTEX_WH

Query timeout

: Enter

300

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Add the Cortex Search Services Tool:

1. Click

Add

next to "Cortex Search Services."

Configure tool details

Name

tasty_bytes_review_search

Description

Searches customer reviews and feedback to identify sentiment, operational issues, and customer satisfaction in

Configure data source location

Configure search result columns

Configure search filters (optional)

6. Click

Add

Schema

: Choose

TB_101.HARMONIZED

Search service

: Choose

TB_101.HARMONIZED.TASTY_BYTES_REVIEW_SEARCH

ID column

: Select

Review

Title column

: Select

TRUCK_BRAND_NAME

Click

Add filter

to add up to 5 optional filters.

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3. Orchestration Tab

4. Access Tab

5. Save Configuration

Step 3 - Access Snowflake CoWork Interface

Orchestration Instruction

Use both Cortex Search and Cortex Analyst to provide unified business intelligence.

Analyze customer feedback sentiment and operational issues from reviews, then correlate findings with revenue

Present insights with revenue quantification and strategic recommendations.

Response Instruction

You are a business intelligence analyst for Tasty Bytes food trucks. When analyzing data:

1. Combine customer review insights with specific revenue and loyalty data to provide comprehensive business i

2. Quantify business impact with specific revenue amounts and market sizes

3. Identify operational risks, competitive threats, and growth opportunities

4. Provide clear, actionable recommendations for executive decision-making

5. Use visualizations when helpful to illustrate business insights

6. Explain the correlation between customer feedback and business performance

7. Focus on strategic insights that drive business outcomes

To control who can use your agent in this lab, you'll simply keep the default ACCOUNTADMIN access, which is sufficient for

testing, with no extra configuration needed; however, you have the option to add more roles, such as TB_ADMIN, by clicking

Add role.

Click

Save

in the top right corner to finalize your agent's configuration.

Your unified intelligence agent is now ready to provide conversational business intelligence through the Snowflake CoWork interface.

With your intelligence agent created, we can now access the Snowflake CoWork interface that provides unified natural language business

intelligence.

Access the interface:

1. Open Snowsight and navigate to the AI & ML Studio, then select

Snowflake CoWork

2. Select our created agent:

tasty_bytes_intelligence_agent

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Step 4 - Correlate Revenue & Customer Themes

Step 5 - Analyze Underperforming Markets

3. Select the sources: select

tasty_bytes_review_search

and

tasty_bytes_business_analytics

You are now ready to demonstrate unified business intelligence through natural language.

Let's deep dive into our highest-earning markets by mapping their financial success to the voice of their customers.

Prompt:

Generate a bar chart displaying the top 5 cities by total revenue. For each of these top-performing cities, an

Key insight:

This analysis really shows off what Snowflake CoWork can do! It helps us connect the dots between how much money our top cities

are making and what our customers in those cities are actually saying. We can quickly see our best-performing markets by revenue, and right

alongside, get a clear picture of the most common things people are talking about in their reviews. This gives us a much richer, more human

understanding of what's truly driving success – or perhaps what subtle issues might be brewing – even in our strongest areas. It's all about making

smarter, more informed decisions, and we get these powerful insights just by asking a simple question.

Now let's explore strategies to address these key customer pain points and develop targeted action plans to improve performance in these cities.

Prompt:

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Conclusion

Governance with Horizon

Overview

What You Will Learn

Identify the 5 cities with the lowest total revenue. For each of these cities, analyze their customer reviews

Key insight:

This analysis from Snowflake CoWork gives us a clear picture of our lowest-earning cities and, crucially, shines a light on the exact

customer pain points that are holding them back. By directly connecting raw revenue numbers with specific feedback from customer reviews, we

can pinpoint where we need to focus our efforts to improve service, product, or support. This provides actionable intelligence to drive targeted

growth and customer satisfaction in these challenged markets, all by simply asking a natural language question.

What we've just experienced with Tasty Bytes showcases a fundamental shift in how businesses can truly understand their data. By seamlessly

integrating Snowflake Cortex Search for deep dives into unstructured customer feedback and Cortex Analyst for conversational insights from

structured business metrics, we've brought a truly unified business intelligence to life.

You saw firsthand the power of this integration: users of all technical levels can now simply ask natural language questions and immediately receive

visually rich, actionable answers. This direct and intuitive access to insights fundamentally transforms how organizations can swiftly identify

operational risks, precisely quantify financial impact, and pinpoint new growth opportunities. It's clear that Snowflake CoWork empowers rapid,

data-driven decision-making, converting what was once fragmented data into clear, compelling business advantage for everyone.

Within this vignette, we will explore some of the powerful governance features within Snowflake Horizon. We will begin with a look at Role-Based

Access Control (RBAC), before diving into features like automated data classification, tag-based masking policies for column-level security, row-

access policies, data quality monitoring, and finally, account-wide security monitoring with the Trust Center.

The fundamentals of Role-Based Access Control (RBAC) in Snowflake.

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What You Will Build

Get the SQL code and paste it into your SQL File.

Roles and Access Control

Overview

Step 1 - Set Context and View Existing Roles

Step 2 - Create a Custom Role

How to automatically classify and tag sensitive data.

How to implement column-level security with Dynamic Data Masking.

How to implement row-level security with Row Access Policies.

How to monitor data quality with Data Metric Functions.

How to monitor account security with the Trust Center.

A custom, privileged role.

A data classification profile for auto-tagging PII.

Tag-based masking policies for string and date columns.

A row access policy to restrict data visibility by country.

A custom Data Metric Function to check data integrity.

Copy and paste the SQL from this

file

in a new SQL File to follow along in Snowflake.

Note that once you've reached the end of the SQL File you can skip to

Step 29 - Apps & Collaboration

Snowflake's security model is built on a framework of Role-based Access Control (RBAC) and Discretionary Access Control (DAC). Access privileges

are assigned to roles, which are then assigned to users. This creates a powerful and flexible hierarchy for securing objects.

Access Control Overview

: To learn more about the key concepts of access control in Snowflake, including securable objects,

roles, privileges, and users.

First, let's set our context for this exercise and view the roles that already exist in the account.

USE ROLE useradmin;

USE DATABASE tb_101;

USE WAREHOUSE tb_dev_wh;

SHOW ROLES;

We will now create a custom

tb_data_steward

role. This role will be responsible for managing and protecting our customer data.

CREATE OR REPLACE ROLE tb_data_steward

COMMENT = 'Custom Role';

The typical hierarchy of system and custom roles might look something like this:

+---------------+

| ACCOUNTADMIN |

+---------------+

^ ^ ^

| | |

+-------------+-+ | ++-------------+

+---------------+ | +--------------+ |

^ | ^ ^ |

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Step 3 - Grant Privileges to the Custom Role

Step 4 - Grant and Use the New Role

| | | | |

+-------+-------+ | | +-----+-------+ +-------+-----+

+---------------+ | | +-------------+ +-------------+

^ | | ^ ^ ^

| | | | | |

| | | | | +-+-----------+

| | | | | +-------------+

| | | | | ^

| | | | | |

+----------+-----+---+--+--------------+-----------+

+----+-----+

| PUBLIC |

+----------+

Snowflake System Defined Role Definitions:

ORGADMIN

: Role that manages operations at the organization level.

ACCOUNTADMIN

: This is the top-level role in the system and should be granted only to a limited/controlled number of users in your account.

SECURITYADMIN

: Role that can manage any object grant globally, as well as create, monitor, and manage users and roles.

USERADMIN

: Role that is dedicated to user and role management only.

SYSADMIN

: Role that has privileges to create warehouses and databases in an account.

PUBLIC

: PUBLIC is a pseudo-role automatically granted to all users and roles. It can own securable objects, and anything it owns becomes

available to every other user and role in the account.

We can't do much with our role without granting privileges to it. Let's switch to the

securityadmin

role to grant our new

tb_data_steward

role

the necessary permissions to use a warehouse and access our database schemas and tables.

USE ROLE securityadmin;

-- Grant warehouse usage

GRANT OPERATE, USAGE ON WAREHOUSE tb_dev_wh TO ROLE tb_data_steward;

-- Grant database and schema usage

GRANT USAGE ON DATABASE tb_101 TO ROLE tb_data_steward;

GRANT USAGE ON ALL SCHEMAS IN DATABASE tb_101 TO ROLE tb_data_steward;

-- Grant table-level privileges

GRANT SELECT ON ALL TABLES IN SCHEMA raw_customer TO ROLE tb_data_steward;

GRANT ALL ON SCHEMA governance TO ROLE tb_data_steward;

GRANT ALL ON ALL TABLES IN SCHEMA governance TO ROLE tb_data_steward;

Finally, we grant the new role to our own user. Then we can switch to the

tb_data_steward

role and run a query to see what data we can access.

-- Grant role to your user

SET my_user = CURRENT_USER();

GRANT ROLE tb_data_steward TO USER IDENTIFIER($my_user);

-- Switch to the new role

USE ROLE tb_data_steward;

-- Run a test query

SELECT TOP 100 * FROM raw_customer.customer_loyalty;

Looking at the query results, it's clear this table contains a lot of Personally Identifiable Information (PII). In the next sections, we'll learn how to

protect it.

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Classification and Auto Tagging

Overview

Step 1 - Create PII Tag and Grant Privileges

Step 2 - Create a Classification Profile

Step 3 - Map Semantic Categories to the PII Tag

Step 4 - Run Classification and View Results

A key first step in data governance is identifying and classifying sensitive data. Snowflake Horizon's auto-tagging capability can automatically

discover sensitive information by monitoring columns in your schemas. We can then use these tags to apply security policies.

Automatic Classification

: Learn how Snowflake can automatically classify sensitive data based on a schedule, simplifying

governance at scale.

Using the

accountadmin

role, we'll create a

pii

tag in our

governance

schema. We will also grant the necessary privileges to our

tb_data_steward

role to perform classification.

USE ROLE accountadmin;

CREATE OR REPLACE TAG governance.pii;

GRANT APPLY TAG ON ACCOUNT TO ROLE tb_data_steward;

GRANT EXECUTE AUTO CLASSIFICATION ON SCHEMA raw_customer TO ROLE tb_data_steward;

GRANT DATABASE ROLE SNOWFLAKE.CLASSIFICATION_ADMIN TO ROLE tb_data_steward;

GRANT CREATE SNOWFLAKE.DATA_PRIVACY.CLASSIFICATION_PROFILE ON SCHEMA governance TO ROLE tb_data_steward;

Now, as the

tb_data_steward

, we'll create a classification profile. This profile defines how auto-tagging will behave.

USE ROLE tb_data_steward;

CREATE OR REPLACE SNOWFLAKE.DATA_PRIVACY.CLASSIFICATION_PROFILE

governance.tb_classification_profile(

{

'minimum_object_age_for_classification_days': 0,

'maximum_classification_validity_days': 30,

'auto_tag': true

});

Next, we'll define a mapping that tells the classification profile to apply our

governance.pii

tag to any column whose

SEMANTIC_CATEGORY

matches common PII types like

NAME

PHONE_NUMBER

EMAIL

, etc.

CALL governance.tb_classification_profile!SET_TAG_MAP(

{'column_tag_map':[

{

'tag_name':'tb_101.governance.pii',

'tag_value':'pii',

'semantic_categories':['NAME', 'PHONE_NUMBER', 'POSTAL_CODE', 'DATE_OF_BIRTH', 'CITY', 'EMAIL']

}]});

Let's manually trigger the classification process on our

customer_loyalty

table. Then, we can query the

INFORMATION_SCHEMA

to see the tags

that were automatically applied.

-- Trigger classification

CALL SYSTEM$CLASSIFY('tb_101.raw_customer.customer_loyalty', 'tb_101.governance.tb_classification_profile');

-- View applied tags

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Masking Policies

Overview

Step 1 - Create Masking Policies

Step 2 - Apply Masking Policies to the Tag

Step 3 - Test the Policies

SELECT

column_name,

tag_database,

tag_schema,

tag_name,

tag_value,

apply_method

FROM TABLE(INFORMATION_SCHEMA.TAG_REFERENCES_ALL_COLUMNS('raw_customer.customer_loyalty', 'table'));

Notice that columns identified as PII now have our custom

governance.pii

tag applied.

Now that our sensitive columns are tagged, we can use Dynamic Data Masking to protect them. A masking policy is a schema-level object that

determines whether a user sees the original data or a masked version at query time. We can apply these policies directly to our

pii

tag.

Column-level Security

: Column-level Security includes Dynamic Data Masking and External Tokenization to protect sensitive

data.

We'll create two policies: one to mask string data and one to mask date data. The logic is simple: if the user's role is not privileged (i.e., not

ACCOUNTADMIN

TB_ADMIN

), return a masked value. Otherwise, return the original value.

-- Create the masking policy for sensitive string data

CREATE OR REPLACE MASKING POLICY governance.mask_string_pii AS (original_value STRING)

RETURNS STRING ->

CASE WHEN

CURRENT_ROLE() NOT IN ('ACCOUNTADMIN', 'TB_ADMIN')

THEN '****MASKED****'

ELSE original_value

END;

-- Now create the masking policy for sensitive DATE data

CREATE OR REPLACE MASKING POLICY governance.mask_date_pii AS (original_value DATE)

RETURNS DATE ->

CASE WHEN

CURRENT_ROLE() NOT IN ('ACCOUNTADMIN', 'TB_ADMIN')

THEN DATE_TRUNC('year', original_value)

ELSE original_value

END;

The power of tag-based governance comes from applying the policy once to the tag. This action automatically protects all columns that have that

tag, now and in the future.

ALTER TAG governance.pii SET

MASKING POLICY governance.mask_string_pii,

MASKING POLICY governance.mask_date_pii;

Let's test our work. First, switch to the unprivileged

public

role and query the table. The PII columns should be masked.

USE ROLE public;

SELECT TOP 100 * FROM raw_customer.customer_loyalty;

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Row Access Policies

Overview

Step 1 - Create a Policy Mapping Table

Step 2 - Create the Row Access Policy

Step 3 - Apply and Test the Policy

Now, switch to a privileged role,

tb_admin

. The data should now be fully visible.

USE ROLE tb_admin;

SELECT TOP 100 * FROM raw_customer.customer_loyalty;

In addition to masking columns, Snowflake allows you to filter which rows are visible to a user with Row Access Policies. The policy evaluates each

row against rules you define, often based on the user's role or other session attributes.

Row-level Security

: Row Access Policies determine which rows are visible in a query result, enabling fine-grained access

control.

A common pattern for row access policies is to use a mapping table that defines which roles can see which data. We'll create a table that maps roles

to the

country

values they are permitted to see.

USE ROLE tb_data_steward;

CREATE OR REPLACE TABLE governance.row_policy_map

(role STRING, country_permission STRING);

-- Map the tb_data_engineer role to only see 'United States' data

INSERT INTO governance.row_policy_map

VALUES('tb_data_engineer', 'United States');

Now we create the policy itself. This policy returns

TRUE

(allowing the row to be seen) if the user's role is an admin role OR if the user's role exists in

our mapping table and matches the

country

value of the current row.

CREATE OR REPLACE ROW ACCESS POLICY governance.customer_loyalty_policy

AS (country STRING) RETURNS BOOLEAN ->

CURRENT_ROLE() IN ('ACCOUNTADMIN', 'SYSADMIN')

OR EXISTS

(

SELECT 1 FROM governance.row_policy_map rp

WHERE

UPPER(rp.role) = CURRENT_ROLE()

AND rp.country_permission = country

);

Apply the policy to the

country

column of our

customer_loyalty

table. Then, switch to the

tb_data_engineer

role and query the table.

-- Apply the policy

ALTER TABLE raw_customer.customer_loyalty

ADD ROW ACCESS POLICY governance.customer_loyalty_policy ON (country);

-- Switch role to test the policy

USE ROLE tb_data_engineer;

-- Query the table

SELECT TOP 100 * FROM raw_customer.customer_loyalty;

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Data Metric Functions

Overview

Step 1 - Use System DMFs

Step 2 - Create a Custom DMF

Step 3 - Test and Schedule the DMF

The result set should now only contain rows where the

country

is 'United States'.

Data governance isn't just about security; it's also about trust and reliability. Snowflake helps maintain data integrity with Data Metric Functions

(DMFs). You can use system-defined DMFs or create your own to run automated quality checks on your tables.

Data Quality Monitoring

: Learn how to ensure data consistency and reliability using built-in and custom Data Metric

Functions.

Let's use a few of Snowflake's built-in DMFs to check the quality of our

order_header

table.

USE ROLE tb_data_steward;

-- This will return the percentage of null customer IDs.

SELECT SNOWFLAKE.CORE.NULL_PERCENT(SELECT customer_id FROM raw_pos.order_header);

-- We can use DUPLICATE_COUNT to check for duplicate order IDs.

SELECT SNOWFLAKE.CORE.DUPLICATE_COUNT(SELECT order_id FROM raw_pos.order_header);

-- Average order total amount for all orders.

SELECT SNOWFLAKE.CORE.AVG(SELECT order_total FROM raw_pos.order_header);

We can also create custom DMFs for our specific business logic. Let's create one that checks for orders where the

order_total

does not equal

unit_price * quantity

CREATE OR REPLACE DATA METRIC FUNCTION governance.invalid_order_total_count(

order_prices_t table(

order_total NUMBER,

unit_price NUMBER,

quantity INTEGER

)

RETURNS NUMBER

'SELECT COUNT(*)

FROM order_prices_t

WHERE order_total != unit_price * quantity';

Let's insert a bad record to test our DMF. Then, we'll call the function to see if it catches the error. The record we will be inserting is ordering 2

items with a unit price of $5, and a total price of $5 instead of the correct total $10.

-- Insert a record with an incorrect total price

INSERT INTO raw_pos.order_detail

SELECT 904745311, 459520442, 52, null, 0, 2, 5.0, 5.0, null;

-- Call the custom DMF on the order detail table.

SELECT governance.invalid_order_total_count(

SELECT price, unit_price, quantity FROM raw_pos.order_detail

) AS num_orders_with_incorrect_price;

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Trust Center

Overview

Step 1 - Grant Privileges and Navigate to the Trust Center

Step 2 - Enable Scanner Packages

To automate this check, we can associate the DMF with the table and set a schedule to have it run automatically whenever the data changes, then

add it to the

order_detail

table.

ALTER TABLE raw_pos.order_detail

SET DATA_METRIC_SCHEDULE = 'TRIGGER_ON_CHANGES';

ALTER TABLE raw_pos.order_detail

ADD DATA METRIC FUNCTION governance.invalid_order_total_count

ON (price, unit_price, quantity);

The Trust Center provides a centralized dashboard for monitoring security risks across your entire Snowflake account. It uses scheduled scanners to

check for issues like missing Multi-Factor Authentication (MFA), over-privileged roles, or inactive users, and then provides recommended actions.

Trust Center Overview

: The Trust Center enables automatic checks to evaluate and monitor security risks on your account.

First, an

ACCOUNTADMIN

needs to grant the

TRUST_CENTER_ADMIN

application role to a user or role. We'll grant it to our

tb_admin

role.

USE ROLE accountadmin;

GRANT APPLICATION ROLE SNOWFLAKE.TRUST_CENTER_ADMIN TO ROLE tb_admin;

USE ROLE tb_admin;

Now, navigate to the Trust Center in the Snowsight UI:

1. Click the

Monitoring

tab in the left navigation bar.

2. Click on

Trust Center

By default, most scanner packages are disabled. Let's enable them to get a comprehensive view of our account's security posture.

1. In the Trust Center, click the

Scanner Packages

tab.

2. Click on

CIS Benchmarks

3. Click the

Enable Package

button.

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Step 3 - Review Findings

4. In the modal, set the

Frequency

Monthly

and click

Continue

5. Repeat this process for the

Threat Intelligence

scanner package.

After the scanners have had a moment to run, navigate back to the

Findings

tab.

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Apps & Collaboration

Overview

What You Will Learn

What You Will Build

You will see a dashboard summarizing violations by severity.

The list below details each violation, its severity, and the scanner that found it.

Clicking on any violation will open a details pane with a summary and recommended remediation steps.

You can filter the list by severity, status, or scanner package to focus on the most critical issues.

This powerful tool gives you a continuous, actionable overview of your Snowflake account's security health.

In this vignette, we will explore how Snowflake facilitates seamless data collaboration through the Snowflake Marketplace. We will see how easy it

is to acquire live, ready-to-query third-party datasets and immediately join them with our own internal data to unlock new insights—all without the

need for traditional ETL pipelines.

How to discover and acquire data from the Snowflake Marketplace.

How to instantly query live, shared data.

How to join Marketplace data with your own account data to create enriched views.

How to leverage third-party Point-of-Interest (POI) data for deeper analysis.

How to use Common Table Expressions (CTEs) to structure complex queries.

Enriched analytical Views that combine internal sales data with external weather and POI data.

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Get the SQL code and paste it into your SQL File.

Acquire Data from Snowflake Marketplace

Overview

Step 1 - Set Initial Context

Step 2 - Acquire Weather Source Data

Copy and paste the SQL code from this

file

in a new SQL File to follow along in Snowflake.

One of our analysts wants to see how weather impacts food truck sales. To do this, they'll use the Snowflake Marketplace to get live weather data

from Weather Source, which can then be joined directly with our own sales data. The Marketplace allows us to access live, ready-to-query data

from third-party providers without any data duplication or ETL.

Introduction to the Snowflake Marketplace

: The Marketplace provides a centralized hub to discover and access a wide variety

of third-party data, applications, and AI products.

First, let's set our context to use the

accountadmin

role, which is required to acquire data from the Marketplace.

USE DATABASE tb_101;

USE ROLE accountadmin;

USE WAREHOUSE tb_de_wh;

Follow these steps in the Snowsight UI to get the Weather Source data:

3. In the search bar, enter:

Weather Source frostbyte

Make sure you are using the

ACCOUNTADMIN

role.

Navigate to

Data Products

Marketplace

from the left-hand navigation menu.

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Integrate Account Data with Shared Data

Overview

Step 1 - Explore the Shared Data

Step 2 - Create an Enriched View

4. Click on the

Weather Source LLC: frostbyte

listing.

Click the

Get

button.

Click to expand the Options, then change the

Database name

ZTS_WEATHERSOURCE

Grant access to the

PUBLIC

role.

Click

Get

This process makes the Weather Source data instantly available in our account as a new database, ready to be queried.

With the Weather Source data now in our account, our analyst can immediately begin joining it with our existing Tasty Bytes data. There's no need

to wait for an ETL job to run.

Let's switch to the

tb_analyst

role and begin exploring the new weather data. We'll start by getting a list of all distinct US cities available in the

share, along with some average weather metrics.

USE ROLE tb_analyst;

SELECT

DISTINCT city_name,

AVG(max_wind_speed_100m_mph) AS avg_wind_speed_mph,

AVG(avg_temperature_air_2m_f) AS avg_temp_f,

AVG(tot_precipitation_in) AS avg_precipitation_in,

MAX(tot_snowfall_in) AS max_snowfall_in

FROM zts_weathersource.onpoint_id.history_day

WHERE country = 'US'

GROUP BY city_name;

Now, let's create a view that joins our raw

country

data with the historical daily weather data from the Weather Source share. This gives us a

unified view of weather metrics for the cities where Tasty Bytes operates.

CREATE OR REPLACE VIEW harmonized.daily_weather_v

COMMENT = 'Weather Source Daily History filtered to Tasty Bytes supported Cities'

SELECT

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Step 3 - Analyze and Visualize Enriched Data

./assets//vignette-5/line_chart.png

Step 4 - Create a Sales and Weather View

hd.*,

TO_VARCHAR(hd.date_valid_std, 'YYYY-MM') AS yyyy_mm,

pc.city_name AS city,

c.country AS country_desc

FROM zts_weathersource.onpoint_id.history_day hd

JOIN zts_weathersource.onpoint_id.postal_codes pc

ON pc.postal_code = hd.postal_code

AND pc.country = hd.country

JOIN raw_pos.country c

ON c.iso_country = hd.country

AND c.city = hd.city_name;

Using our new view, the analyst can query for the average daily temperature in Hamburg, Germany for February 2022. Run the query below, then

we'll visualize this as a line chart directly in Snowsight.

SELECT

dw.country_desc,

dw.city_name,

dw.date_valid_std,

AVG(dw.avg_temperature_air_2m_f) AS average_temp_f

FROM harmonized.daily_weather_v dw

WHERE dw.country_desc = 'Germany'

AND dw.city_name = 'Hamburg'

AND YEAR(date_valid_std) = 2022

AND MONTH(date_valid_std) = 2

GROUP BY dw.country_desc, dw.city_name, dw.date_valid_std

ORDER BY dw.date_valid_std DESC;

1. Run the query above.

2. In the

Results

pane, click

Chart

3. Set the

Chart Type

Line

4. Set the

X-Axis

DATE_VALID_STD

5. Set the

Y-Axis

AVERAGE_TEMP_F

Let's take it a step further and combine our

orders_v

view with our new

daily_weather_v

to see how sales correlate with weather conditions.

CREATE OR REPLACE VIEW analytics.daily_sales_by_weather_v

COMMENT = 'Daily Weather Metrics and Orders Data'

WITH daily_orders_aggregated AS (

SELECT DATE(o.order_ts) AS order_date, o.primary_city, o.country,

o.menu_item_name, SUM(o.price) AS total_sales

FROM harmonized.orders_v o

GROUP BY ALL

)

SELECT

dw.date_valid_std AS date, dw.city_name, dw.country_desc,

ZEROIFNULL(doa.total_sales) AS daily_sales, doa.menu_item_name,

ROUND(dw.avg_temperature_air_2m_f, 2) AS avg_temp_fahrenheit,

ROUND(dw.tot_precipitation_in, 2) AS avg_precipitation_inches,

ROUND(dw.tot_snowdepth_in, 2) AS avg_snowdepth_inches,

dw.max_wind_speed_100m_mph AS max_wind_speed_mph

FROM harmonized.daily_weather_v dw

LEFT JOIN daily_orders_aggregated doa

ON dw.date_valid_std = doa.order_date

AND dw.city_name = doa.primary_city

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Step 5 - Answer a Business Question

Explore Point-of-Interest Data

Overview

Step 1 - Acquire Safegraph POI Data

AND dw.country_desc = doa.country

ORDER BY date ASC;

Our analyst can now answer complex business questions, such as: "How does significant precipitation impact our sales figures in the Seattle

market?"

SELECT * EXCLUDE (city_name, country_desc, avg_snowdepth_inches, max_wind_speed_mph)

FROM analytics.daily_sales_by_weather_v

WHERE

country_desc = 'United States'

AND city_name = 'Seattle'

AND avg_precipitation_inches >= 1.0

ORDER BY date ASC;

Let's also visualize the results again in Snowsight, but as a bar chart this time.

1. Run the query above.

2. In the

Results

pane, click

Chart

3. Set the

Chart Type

Bar

4. Set the

X-Axis

MENU_ITEM_NAME

5. Set the

Y-Axis

DAILY_SALES

Our analyst now wants more insight into the specific locations of our food trucks. We can get Point-of-Interest (POI) data from Safegraph, another

provider on the Snowflake Marketplace, to enrich our analysis even further.

Follow the same procedure as before to acquire the Safegraph data from the Marketplace.

1. Ensure you are using the

ACCOUNTADMIN

role.

2. Navigate to

Data Products

Marketplace

3. In the search bar, enter:

safegraph frostbyte

4. Select the

Safegraph: frostbyte

listing and click

Get

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Step 2 - Create a POI View

Step 3 - Combine POI and Weather Data

Step 4 - Analyze Brand Resilience to Weather

5. Click to expand the Options, then set the

Database name

ZTS_SAFEGRAPH

6. Grant access to the

PUBLIC

role.

7. Click

Get

Let's create a view that joins our internal

location

data with the Safegraph POI data.

CREATE OR REPLACE VIEW harmonized.tastybytes_poi_v

SELECT

l.location_id, sg.postal_code, sg.country, sg.city, sg.iso_country_code,

sg.location_name, sg.top_category, sg.category_tags,

sg.includes_parking_lot, sg.open_hours

FROM raw_pos.location l

JOIN zts_safegraph.public.frostbyte_tb_safegraph_s sg

ON l.location_id = sg.location_id

AND l.iso_country_code = sg.iso_country_code;

Now we can combine all three datasets: our internal data, the weather data, and the POI data. Let's find our top 3 windiest truck locations in the US

in 2022.

SELECT TOP 3

p.location_id, p.city, p.postal_code,

AVG(hd.max_wind_speed_100m_mph) AS average_wind_speed

FROM harmonized.tastybytes_poi_v AS p

JOIN zts_weathersource.onpoint_id.history_day AS hd

ON p.postal_code = hd.postal_code

WHERE

p.country = 'United States'

AND YEAR(hd.date_valid_std) = 2022

GROUP BY p.location_id, p.city, p.postal_code

ORDER BY average_wind_speed DESC;

Finally, let's conduct a more complex analysis to determine brand resilience. We'll use a Common Table Expression (CTE) to first find the windiest

locations, and then compare sales on "calm" vs. "windy" days for each truck brand at those locations. This can help inform operational decisions, like

offering "Windy Day" promotions for brands that are less resilient.

WITH TopWindiestLocations AS (

SELECT TOP 3

p.location_id

FROM harmonized.tastybytes_poi_v AS p

JOIN zts_weathersource.onpoint_id.history_day AS hd ON p.postal_code = hd.postal_code

WHERE p.country = 'United States' AND YEAR(hd.date_valid_std) = 2022

GROUP BY p.location_id, p.city, p.postal_code

ORDER BY AVG(hd.max_wind_speed_100m_mph) DESC

)

SELECT

o.truck_brand_name,

ROUND(AVG(CASE WHEN hd.max_wind_speed_100m_mph <= 20 THEN o.order_total END), 2) AS avg_sales_calm_days,

ZEROIFNULL(ROUND(AVG(CASE WHEN hd.max_wind_speed_100m_mph > 20 THEN o.order_total END), 2)) AS avg_sales_w

FROM analytics.orders_v AS o

JOIN zts_weathersource.onpoint_id.history_day AS hd

ON o.primary_city = hd.city_name AND DATE(o.order_ts) = hd.date_valid_std

WHERE o.location_id IN (SELECT location_id FROM TopWindiestLocations)

GROUP BY o.truck_brand_name

ORDER BY o.truck_brand_name;

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Introduction to Streamlit in Snowflake

Step 1 - Create Streamlit App

Conclusion and Resources

Streamlit is an open-source Python library designed for easily creating and sharing web applications for machine learning and data science. It allows

for the rapid development and deployment of data-driven apps.

Streamlit in Snowflake empowers developers to securely build, deploy, and share applications directly within Snowflake. This integration allows you

to build apps that process and utilize data stored in Snowflake without the need of moving the data or application code to an external system.

Let's create our first Streamlit app, an app that will display and chart sales data for each menu item in Japan for February 2022.

First, navigate to

Projects

Streamlit

, then click on the blue '+ Streamlit App' button in the top right to create a new app.

Enter these values in the 'Create Streamlit App' pop-up:

App title: Menu Item Sales

App location:

Database: tb_101

Schema: Analytics

App warehouse: tb_dev_wh

Now click 'Create'. When the app first loads, you'll see a sample app on the right pane and the app's code in the editor pane to the left.

Select all of the code and remove it.

Next copy + paste this

code

in the blank editor window, then click 'Run' in the top right.

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Overview

What You Learned

Resources

Congratulations! You have successfully completed the entire Tasty Bytes - Zero to Snowflake journey.

You have now built and configured warehouses, cloned and transformed data, recovered a dropped table with Time Travel, and built an automated

data pipeline for semi-structured data. You've also unlocked insights using AI by generating analysis with simple AISQL functions and accelerating

your workflow with Snowflake Copilot. Furthermore, you have implemented a robust governance framework with roles and policies and seamlessly

enriched your own data with live datasets from the Snowflake Marketplace.

If you would like to re-run this Quickstart, please run the complete

RESET

script located at the bottom of your SQL file.

Warehousing and Performance:

How to create, manage, and scale virtual warehouses, and leverage Snowflake's results cache.

Data Transformation:

How to use Zero-Copy Cloning for safe development, transform data, and instantly recover from errors using Time Travel

and

UNDROP

Data Pipelines:

How to ingest data from external stages, process semi-structured

VARIANT

data, and build automated ELT pipelines with

Dynamic Tables.

Snowflake Cortex AI

How to leverage Snowflake Cortex AI to build a customer analytics platform.

Data Governance:

How to implement a security framework using Role-Based Access Control, automated PII classification, tag-based Data

Masking, and Row Access Policies.

Data Collaboration:

How to discover and acquire live, third-party datasets from the Snowflake Marketplace and seamlessly join them with your

own data to generate new insights.

Virtual Warehouses & Settings

Resource Monitors

Budgets

Universal Search

Ingestion from External Stage

Semi-Structured Data

Dynamic Tables

Roles & Access Control

Tag-Based Classification

Column Level Security with Masking Policies

Row Level Security with Row Access Policies

Data Metric Functions

Trust Center

Data Sharing

Snowflake Cortex Playground

AI SQL Functions in Snowflake Cortex

Snowflake Cortex Search Overview

Snowflake Cortex Analyst

Updated Dec 11, 2025

This content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances

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The Zero to Snowflake Quickstart Guide

Prompt to Clone This The Zero to Snowflake Quickstart Guide

Apply this example's core structure, strategic pacing, and structural hierarchy directly to your own topic.

What are you generating today?