By completing this guide, you will be able to go from raw data to an interactive application that can help organization optimize their advertising budget allocation.

Here is a summary of what you will be able to learn in each step by following this quickstart:

In case you are new to some of the technologies mentioned above, here's a quick summary with links to documentation.

What is Snowpark?

Snowpark is the set of libraries and runtimes that securely enable developers to deploy and process Python code in Snowflake.

Client Side Libraries - Snowpark libraries can be installed and downloaded from any client-side notebook or IDE and are used for code development and deployment. Libraries include the Snowpark API for data pipelines and apps and the Snowpark ML API for end to end machine learning.

Elastic Compute Runtimes - Snowpark provides elastic compute runtimes for secure execution of your code in Snowflake. Runtimes include Python, Java, and Scala in virtual warehouses with CPU compute or Snowpark Container Services (public preview) to execute any language of choice with CPU or GPU compute.

Learn more about Snowpark.

What is Snowpark ML?

Snowpark ML includes the Python library and underlying infrastructure for end-to-end ML workflows in Snowflake. With Snowpark ML, data scientists and ML engineers can use familiar Python frameworks for preprocessing, feature engineering, and training models that can be managed entirely in Snowflake without any data movement, silos or governance trade-offs. Snowpark ML has 2 components: Snowpark ML Modeling for model development and Snowpark ML Operations including the Snowpark Model Registry (public preview) for model management and batch inference.

Snowpark

This quickstart will focus on

Feature Engineering and Preprocessing - Improve performance and scalability with distributed execution for common scikit-learn preprocessing functions.

Model Training - Accelerate model training for scikit-learn, XGBoost and LightGBM models without the need to manually create stored procedures or user-defined functions (UDFs), and leverage distributed hyperparameter optimization (public preview).

Snowpark

Model Management and Batch Inference - Manage several types of ML models created both within and outside Snowflake and execute batch inference.

Snowpark

What is Streamlit?

Streamlit enables data scientists and Python developers to combine Streamlit's component-rich, open-source Python library with the scale, performance, and security of the Snowflake platform.

Learn more about Streamlit.

What You Will Learn

Prerequisites

Create Tables, Load Data and Setup Stages

Log into Snowsight using your credentials to create tables, load data from Amazon S3, and setup Snowflake internal stages.

Run the following SQL commands to create the warehouse, database and schema.

USE ROLE ACCOUNTADMIN;

CREATE OR REPLACE WAREHOUSE DASH_L WAREHOUSE_SIZE=LARGE;
CREATE OR REPLACE DATABASE DASH_DB;
CREATE OR REPLACE SCHEMA DASH_SCHEMA;

USE DASH_DB.DASH_SCHEMA;

Run the following SQL commands to create table CAMPAIGN_SPEND from data hosted on publicly accessible S3 bucket.

CREATE or REPLACE file format csvformat
  skip_header = 1
  type = 'CSV';

CREATE or REPLACE stage campaign_data_stage
  file_format = csvformat
  url = 's3://sfquickstarts/ad-spend-roi-snowpark-python-scikit-learn-streamlit/campaign_spend/';

CREATE or REPLACE TABLE CAMPAIGN_SPEND (
  CAMPAIGN VARCHAR(60), 
  CHANNEL VARCHAR(60),
  DATE DATE,
  TOTAL_CLICKS NUMBER(38,0),
  TOTAL_COST NUMBER(38,0),
  ADS_SERVED NUMBER(38,0)
);

COPY into CAMPAIGN_SPEND
  from @campaign_data_stage;

Run the following SQL commands to create table MONTHLY_REVENUE from data hosted on publicly accessible S3 bucket.

CREATE or REPLACE stage monthly_revenue_data_stage
  file_format = csvformat
  url = 's3://sfquickstarts/ad-spend-roi-snowpark-python-scikit-learn-streamlit/monthly_revenue/';

CREATE or REPLACE TABLE MONTHLY_REVENUE (
  YEAR NUMBER(38,0),
  MONTH NUMBER(38,0),
  REVENUE FLOAT
);

COPY into MONTHLY_REVENUE
  from @monthly_revenue_data_stage;

Run the following SQL commands to create table BUDGET_ALLOCATIONS_AND_ROI that holds the last six months of budget allocations and ROI.

CREATE or REPLACE TABLE BUDGET_ALLOCATIONS_AND_ROI (
  MONTH varchar(30),
  SEARCHENGINE integer,
  SOCIALMEDIA integer,
  VIDEO integer,
  EMAIL integer,
  ROI float
);

INSERT INTO BUDGET_ALLOCATIONS_AND_ROI (MONTH, SEARCHENGINE, SOCIALMEDIA, VIDEO, EMAIL, ROI)
VALUES
('January',35,50,35,85,8.22),
('February',75,50,35,85,13.90),
('March',15,50,35,15,7.34),
('April',25,80,40,90,13.23),
('May',95,95,10,95,6.246),
('June',35,50,35,85,8.22);

Run the following commands to create Snowflake internal stages for storing Stored Procedures, UDFs, and ML model files.

CREATE OR REPLACE STAGE dash_sprocs;
CREATE OR REPLACE STAGE dash_models;
CREATE OR REPLACE STAGE dash_udfs;

Optionally, you can also open setup.sql in Snowsight and run all SQL statements to create the objects and load data from AWS S3.

This section covers cloning of the GitHub repository and setting up your Snowpark for Python environment.

Clone GitHub Repository

The very first step is to clone the GitHub repository. This repository contains all the code you will need to successfully complete this QuickStart Guide.

Using HTTPS:

git clone https://github.com/Snowflake-Labs/sfguide-getting-started-dataengineering-ml-snowpark-python.git

OR, using SSH:

git clone git@github.com:Snowflake-Labs/sfguide-getting-started-dataengineering-ml-snowpark-python.git

Snowpark for Python

To complete the Data Engineering and Machine Learning steps, you have the option to either install everything locally (option 1) or use Hex (option 2) as described below.

Option 1 – Local Installation

Step 1: Download and install the miniconda installer from https://conda.io/miniconda.html. (OR, you may use any other Python environment with Python 3.9, for example, virtualenv).

Step 2: Open a new terminal window and execute the following commands in the same terminal window.

Step 3: Create Python 3.9 conda environment called snowpark-de-ml by running the following command in the same terminal window

conda create --name snowpark-de-ml -c https://repo.anaconda.com/pkgs/snowflake python=3.9

Step 4: Activate conda environment snowpark-de-ml by running the following command in the same terminal window

conda activate snowpark-de-ml

Step 5: Install Snowpark Python, Snowpark ML, and other libraries in conda environment snowpark-de-ml from Snowflake Anaconda channel by running the following command in the same terminal window

conda install -c https://repo.anaconda.com/pkgs/snowflake snowflake-snowpark-python snowflake-ml-python pandas notebook cachetools

Step 6: Update connection.json with your Snowflake account details and credentials.

Here's a sample connection.json based on the object names mentioned in Setup Environment step.

{
  "account"   : "<your_account_identifier_goes_here>",
  "user"      : "<your_username_goes_here>",
  "password"  : "<your_password_goes_here>",
  "role"      : "ACCOUNTADMIN",
  "warehouse" : "DASH_L",
  "database"  : "DASH_DB",
  "schema"    : "DASH_SCHEMA"
}

Option 2 – Use Hex

If you choose to use your existing Hex account or create a free 30-day trial account, then Snowpark for Python is built-in so you don't have to create a Python environment and install Snowpark for Python along with other libraries locally on your laptop. This will enable you to complete Data Engineering and Machine Learning steps of this QuickStart Guide directly in Hex. (See the respective steps for details on loading the Data Engineering and Machine Learning notebooks in Hex.)

The Notebook linked below covers the following data engineering tasks.

  1. Establish secure connection from Snowpark Python to Snowflake
  2. Load data from Snowflake tables into Snowpark DataFrames
  3. Perform Exploratory Data Analysis on Snowpark DataFrames
  4. Pivot and Join data from multiple tables using Snowpark DataFrames
  5. Automate data pipeline tasks using Snowflake Tasks

Data Engineering Notebook in Jupyter or Visual Studio Code

To get started, follow these steps:

  1. In a terminal window, browse to this folder and run jupyter notebook at the command line. (You may also use other tools and IDEs such Visual Studio Code.)
  2. Open and run through the cells in Snowpark_For_Python_DE.ipynb

Data Engineering Notebook in Hex

If you choose to use your existing Hex account or create a free 30-day trial account, follow these steps to load the notebook and create a data connection to connect to Snowflake from Hex.

  1. Import Snowpark_For_Python_DE.ipynb as a Project in your account. For more information on importing, refer to the docs.
  2. Then, instead of using the connection.json to connect to Snowflake, create a Data Connection and use that in the Data Engineering Notebook as shown below.

HEX Data Connection

  1. Replace the following code snippet in the notebook
connection_parameters = json.load(open('connection.json'))
session = Session.builder.configs(connection_parameters).create()

with...

import hextoolkit
hex_snowflake_conn = hextoolkit.get_data_connection('YOUR_DATA_CONNECTION_NAME')
session = hex_snowflake_conn.get_snowpark_session()
session.sql('USE SCHEMA DASH_SCHEMA').collect()

You can also operationalize the data transformations in the form of automated data pipelines running in Snowflake.

In particular, in the Data Engineering Notebook, there's a section that demonstrates how to optionally build and run the data transformations as Snowflake Tasks.

For reference purposes, here are the code snippets.

Root/parent Task

This task automates loading campain spend data and performing various transformations.

def campaign_spend_data_pipeline(session: Session) -> str:
  # DATA TRANSFORMATIONS
  # Perform the following actions to transform the data

  # Load the campaign spend data
  snow_df_spend_t = session.table('campaign_spend')

  # Transform the data so we can see total cost per year/month per channel using group_by() and agg() Snowpark DataFrame functions
  snow_df_spend_per_channel_t = snow_df_spend_t.group_by(year('DATE'), month('DATE'),'CHANNEL').agg(sum('TOTAL_COST').as_('TOTAL_COST')).\
      with_column_renamed('"YEAR(DATE)"',"YEAR").with_column_renamed('"MONTH(DATE)"',"MONTH").sort('YEAR','MONTH')

  # Transform the data so that each row will represent total cost across all channels per year/month using pivot() and sum() Snowpark DataFrame functions
  snow_df_spend_per_month_t = snow_df_spend_per_channel_t.pivot('CHANNEL',['search_engine','social_media','video','email']).sum('TOTAL_COST').sort('YEAR','MONTH')
  snow_df_spend_per_month_t = snow_df_spend_per_month_t.select(
      col("YEAR"),
      col("MONTH"),
      col("'search_engine'").as_("SEARCH_ENGINE"),
      col("'social_media'").as_("SOCIAL_MEDIA"),
      col("'video'").as_("VIDEO"),
      col("'email'").as_("EMAIL")
  )

  # Save transformed data
  snow_df_spend_per_month_t.write.mode('overwrite').save_as_table('SPEND_PER_MONTH')

# Register data pipelining function as a Stored Procedure so it can be run as a task
session.sproc.register(
  func=campaign_spend_data_pipeline,
  name="campaign_spend_data_pipeline",
  packages=['snowflake-snowpark-python'],
  is_permanent=True,
  stage_location="@dash_sprocs",
  replace=True)

campaign_spend_data_pipeline_task = """
CREATE OR REPLACE TASK campaign_spend_data_pipeline_task
    WAREHOUSE = 'DASH_L'
    SCHEDULE  = '3 MINUTE'
AS
    CALL campaign_spend_data_pipeline()
"""
session.sql(campaign_spend_data_pipeline_task).collect()

Child/dependant Task

This task automates loading monthly revenue data, performing various transformations, and joining it with transformed campaign spend data.

def monthly_revenue_data_pipeline(session: Session) -> str:
  # Load revenue table and transform the data into revenue per year/month using group_by and agg() functions
  snow_df_spend_per_month_t = session.table('spend_per_month')
  snow_df_revenue_t = session.table('monthly_revenue')
  snow_df_revenue_per_month_t = snow_df_revenue_t.group_by('YEAR','MONTH').agg(sum('REVENUE')).sort('YEAR','MONTH').with_column_renamed('SUM(REVENUE)','REVENUE')

  # Join revenue data with the transformed campaign spend data so that our input features (i.e. cost per channel) and target variable (i.e. revenue) can be loaded into a single table for model training
  snow_df_spend_and_revenue_per_month_t = snow_df_spend_per_month_t.join(snow_df_revenue_per_month_t, ["YEAR","MONTH"])

  # SAVE in a new table for the next task
  snow_df_spend_and_revenue_per_month_t.write.mode('overwrite').save_as_table('SPEND_AND_REVENUE_PER_MONTH')

# Register data pipelining function as a Stored Procedure so it can be run as a task
session.sproc.register(
  func=monthly_revenue_data_pipeline,
  name="monthly_revenue_data_pipeline",
  packages=['snowflake-snowpark-python'],
  is_permanent=True,
  stage_location="@dash_sprocs",
  replace=True)

monthly_revenue_data_pipeline_task = """
  CREATE OR REPLACE TASK monthly_revenue_data_pipeline_task
      WAREHOUSE = 'DASH_L'
      AFTER campaign_spend_data_pipeline_task
  AS
      CALL monthly_revenue_data_pipeline()
  """
session.sql(monthly_revenue_data_pipeline_task).collect()

Start Tasks

Snowflake Tasks are not started by default so you need to execute the following statements to start/resume them.

session.sql("alter task monthly_revenue_data_pipeline_task resume").collect()
session.sql("alter task campaign_spend_data_pipeline_task resume").collect()

Suspend Tasks

If you resume the above tasks, suspend them to avoid unecessary resource utilization by executing the following commands.

session.sql("alter task campaign_spend_data_pipeline_task suspend").collect()
session.sql("alter task monthly_revenue_data_pipeline_task suspend").collect()

Tasks Observability

These tasks and their DAGs can be viewed in Snowsight as shown below.

Tasks-Observability

Error Notificatons For Tasks

You can also enable push notifications to a cloud messaging service when errors occur while tasks are being executed. For more information, please refer to the documentation.

The Notebook linked below covers the following machine learning tasks.

  1. Establish secure connection from Snowpark Python to Snowflake
  2. Load features and target from Snowflake table into Snowpark DataFrame
  3. Prepare features for model training
  4. Train ML model using Snowpark ML on Snowflake
  5. Create Scalar and Vectorized (aka Batch) Python User-Defined Functions (UDFs) for inference on new data points for online and offline inference respectively.

End-To-End-ML

Machine Learning Notebook in Jupyter or Visual Studio Code

To get started, follow these steps:

  1. In a terminal window, browse to this folder and run jupyter notebook at the command line. (You may also use other tools and IDEs such Visual Studio Code.)
  2. Open and run through the Snowpark_For_Python_ML.ipynb

Machine Learning Notebook in Hex

If you choose to use your existing Hex account or create a free 30-day trial account, follow these steps to load the notebook and create a data connection to connect to Snowflake from Hex.

  1. Import Snowpark_For_Python_ML.ipynb as a Project in your account. For more information on importing, refer to the docs.
  2. Then, instead of using the connection.json to connect to Snowflake, create a Data Connection and use that in the Machine Learning Notebook as shown below.

HEX Data Connection

  1. Replace the following code snippet in the notebook
connection_parameters = json.load(open('connection.json'))
session = Session.builder.configs(connection_parameters).create()

with...

import hextoolkit
hex_snowflake_conn = hextoolkit.get_data_connection('YOUR_DATA_CONNECTION_NAME')
session = hex_snowflake_conn.get_snowpark_session()
session.sql('USE SCHEMA DASH_SCHEMA').collect()

Follow these steps to build Streamlit application in Snowsight.

Step 1. Click on Streamlit on the left navigation menu

Step 2. Click on + Streamlit App on the top right

Step 3. Enter App title

Step 4. Select App location (DASH_DB and DASH_SCHEMA) and App warehouse (DASH_L)

Step 5. Click on Create

Step 6. Replace sample application code displayed in the code editor on the left with the code provided in Snowpark_Streamlit_Revenue_Prediction_SiS.py

Step 7. Click on Run on the top right

If all goes well, you should see the application in Snowsight as shown below.

Streamlit-in-Snowflake

Step 8. Save data to Snowflake

In the application, adjust the advertising budget sliders to see the predicted ROI for those allocations. You can also click on Save to Snowflake button to save the current allocations and predicted ROI into BUDGET_ALLOCATIONS_AND_ROI Snowflake table.

If you started/resumed the two tasks monthly_revenue_data_pipeline_task and campaign_spend_data_pipeline_task as part of the Data Engineering or Data Pipelines sections, then it is important that you run the following commands to suspend those tasks in order to avoid unecessary resource utilization.

In Notebook using Snowpark Python API

session.sql("alter task campaign_spend_data_pipeline_task suspend").collect()
session.sql("alter task monthly_revenue_data_pipeline_task suspend").collect()

In Snowsight

alter task campaign_spend_data_pipeline_task suspend;
alter task monthly_revenue_data_pipeline_task suspend;

Congratulations! You've successfully performed data engineering tasks and trained a Linear Regression model to predict future ROI (Return On Investment) of variable advertising spend budgets across multiple channels including Search, Video, Social Media, and Email using Snowpark for Python and scikit-learn. And then you created a Streamlit application that uses that model to generate predictions on new budget allocations based on user input.

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What You Learned

Related Resources