Snowpark on Jupyter Getting Started Guide

This project will demonstrate how to get started with Jupyter Notebooks on Snowpark, a new product feature announced by Snowflake for public preview during the 2021 Snowflake Summit. You will learn how to tackle real world business problems as straightforward as ELT processing but also as diverse as math with rational numbers with unbounded precision, sentiment analysis and machine learning.

Snowpark not only works with Jupyter Notebooks but with a variety of IDEs. Instructions on how to set up your favorite development environment can be found in the Snowpark documentation under Setting Up Your Development Environment for Snowpark.

Snowpark

Snowpark is a new developer framework of Snowflake. It brings deeply integrated, DataFrame-style programming to the languages developers like to use, and functions to help you expand more data use cases easily, all executed inside of Snowflake. Snowpark support starts with Scala API, Java UDFs, and External Functions.

With Snowpark, developers can program using a familiar construct like the DataFrame, and bring in complex transformation logic through UDFs, and then execute directly against Snowflake’s processing engine, leveraging all of its performance and scalability characteristics in the Data Cloud.

Snowpark provides several benefits over how developers have designed and coded data driven solutions in the past:

Simplifies architecture and data pipelines by bringing different data users to the same data platform, and process against the same data without moving it around.
Accelerates data pipeline workloads by executing with performance, reliability, and scalability with Snowflake’s elastic performance engine.
Eliminates maintenance and overhead with managed services and near-zero maintenance.
Creates a single governance framework and a single set of policies to maintain by using a single platform.
Provides a highly secure environment with administrators having full control over which libraries are allowed to execute inside the Java/Scala runtimes for Snowpark.

The following tutorial highlights these benefits and lets you experience Snowpark in your environment.

Tutorial

This repo is structured in multiple parts. Each part has a notebook with specific focus areas. All notebooks in this series require a Jupyter Notebook environment with a Scala kernel.

All notebooks will be fully self contained, meaning that all you need for processing and analyzing datasets is a Snowflake account. If you do not have a Snowflake account, you can sign up for a free trial. It doesn't even require a credit card.

Versions used in this notebook are up-to-date as of August 2021.

Part 1

The first notebook in this series provides a quick-start guide and an introduction to the Snowpark DataFrame API. The notebook explains the steps for setting up the environment (REPL), and how to resolve dependencies to Snowpark. After a simple "Hello World" example you will learn about the Snowflake DataFrame API, projections, filters, and joins.
Part 2

The second notebook in the series builds on the quick-start of the first part. Using the TPCH dataset in the sample database, it shows how to use aggregations and pivot functions in the Snowpark DataFrame API. Then it introduces UDFs and how to build a stand-alone UDF: a UDF that only uses standard primitives. From there, we will learn how to use third party Scala libraries to perform much more complex tasks like math for numbers with unbounded (unlimited number of significant digits) precision and how to perform sentiment analysis on an arbitrary string.
Part 3

The third notebook combines what you learned in part 1 and 2. It implements an end-to-end ML use case including data ingestion, ETL/ELT transformations, model training, model scoring, and result visualization.

Building a runtime environment

There are 2 options for running the different notebooks. You can either run them on a docker container (locally or cloud based), or you can run them via a hosted notebook service. As an example I am showing how to run the tutorial on AWS SageMaker Notebooks.

Running Jupyter locally

The following instructions show how to build a Notebook server using a Docker container.

Download and install Docker.

Clone the Github Lab Repo:

 cd ~
 mkdir DockerImages
 
 cd DockerImages
 git clone https://github.com/Snowflake-Labs/sfguide_snowpark_on_jupyter.git

Build the Docker container

 cd ~/DockerImages/sfguide_snowpark_on_jupyter/docker
 docker build -t snowparklab .

Starting your Jupyter environment

Type the following commands to start the container and mount the Snowpark Lab directory to the container. The command below assumes that you have cloned the git repo to ~/DockerImages/sfguide_snowpark_on_jupyterJupyter. Adjust the path if necessary.

 cd ~/DockerImages/sfguide_snowpark_on_jupyter
 docker run -it --rm -p 8888:8888 -e JUPYTER_ENABLE_LAB=yes -v "$(pwd)":/home/jovyan/snowparklab --name snowparklab snowparklab

The output should be similar to the following

 To access the server, open this file in a browser:
     file:///home/jovyan/.local/share/jupyter/runtime/jpserver-15-open.html
 Or copy and paste one of these URLs:
     http://162e383e431c:8888/lab?token=bdaead06c9944057a86f9d8a823cebad4ce66799d855be5d
     http://127.0.0.1:8888/lab?token=bdaead06c9944057a86f9d8a823cebad4ce66799d855be5d

Start a browser session (Safari, Chrome, ...). Paste the line with the local host address (127.0.0.1) printed in your shell window into the browser status bar and update the port (8888) to your port in case you have changed the port in the step above.
Stopping your Jupyter environment

Type the following command into a new shell window when you want to stop your the tutorial. All changes/work will be saved on your local machine.
```
 docker stop snowparklab
```
This command will stop and then delete the container. When you want to restart the tutorial, just run the commands above in Starting your Jupyter environment.

Running Jupyter in a hosted environment

In case you can't install docker on your local machine you could run the tutorial in AWS on an AWS Notebook Instance.

Create a Notebook instance
Create a Lifecycle Policy.

Open the lifecycle script and paste its content into the editor window.

Creating the Notebook takes about 8 minutes.
Upload the tutorial folder (github repo zipfile)
Unzip folder

Open the Launcher, start a terminal window and run the command below (substitue with your filename).
```
 unzip SageMaker/<filename> -d SageMaker/
```