Overview
This one‐day course covers key Snowpark features for developing applications in Snowflake and is intended for practitioners who will be building Snowpark application solutions in Snowflake.
The emphasis of this course is on a variety of application developer capabilities rather than core programming skills.
The course consists of lectures, demonstrations, labs, and discussions.
Skills Covered
- Describe Snowflake’s notebook capabilities.
- Create and work with Snowflake Notebooks.
- Create reusable code as User-defined Table Functions (UDTFs).
- Use DML to create and manage tables.
- Solve problems with Snowsight Python Worksheets.
- Record the activity of your Snowflake programs with logging.
- Develop competency in using Pandas on Snowflake.
- Process unstructured data using Snowflake User-defined Functions (UDFs) and stored procedures.
Prerequisites
- Recommended completion of the “Snowflake Multi-Factor Authentication (MFA) Essentials” free on-demand course.
- Completion of “Snowpark DataFrame Programming” or prior knowledge and experience with DataFrame programming, including:
- Creation
- Transformation
- Actions
- PySpark
- UDFs
- Stored Procedures
- Prior knowledge and experience with Snowflake accounts, roles, virtual warehouses, databases, tables, and views.
- Previous data warehouse knowledge and experience.
- Proficiency in writing code in Python.
- Familiarity with Snowflake objects and basic SQL.
Target Audience
- Data Engineers
- Data Scientists
- Data Application Developers
- Database Architects
- Database Administrators
- Data Analysts with programming experience
Introduction to Snowflake Notebooks
- Interactive Cell-Based Programming
- Creating a Notebook
- Snowflake Notebooks Template
- Notebook Cell Basics
- Running Notebook Cells
- Editing Cells
Snowpark Review
- What is Snowpark?
- Snowpark Uses
- Snowpark Architecture
- Snowpark Setup
DML Using Table DataFrames
- The DataFrame and Table Objects
- Creating a Table Object
- Deleting Rows From a Table Object
- Updating Rows in a Table
- Merging Rows into a Table
- Understanding Views
User-Defined Functions (UDFs) Recap and Developing User-Defined Table Functions (UDTFs)
- User-Defined Functions (UDFs) Review
- User-Defined Table Functions (UDTFs) Overview
- Creating Python User-Defined Table Functions (UDTFs)
- Invoking Python UDTFs
- Creating a Python UDTF from a Python File or Module
Developing Stored Procedures
- Stored Procedure Handler Function
- Registering the Stored Procedure
- Temporary, Anonymous, and Permanent Stored Procedures
- Table Procedures a.k.a Tabular Procedures
- Using Custom and Third-Party Libraries
- Authoring a Stored Procedure Using DDL
- Stored Procedure Profiler
Working with Snowsight Python Worksheets
- New Python Worksheet
- Template Code
- Run Output
- Run Results
- Deploy to Stored Procedure
- Test Handler
Python Vectorized UDFs
- Create a Vectorized UDF with the Vectorized Decorator
- Create a Vectorized UDF Using Function Attributes
- Using pandas.Series vs pandas.DataFrame
Processing Unstructured Data with Snowpark
- Steps in Processing Unstructured Data
- Stages
- Directory Tables
- File Access URLs
- The FILE Data Type
- Encryption for Internal Stages
- Processing Unstructured Data in UDFs
- Unstructured Data Best Practices
Logging Messages With Snowpark
- Logging Introduction
- Log Entries vs Trace Events
- Event Tables
- Setting Log Levels
- Logging From Objects
- Emitting Trace Events
- Unhandled Exception Events
- Querying Over Log and Trace Messages
- Viewing Logs and Traces in Snowsight
Introducing pandas on Snowflake
- What is pandas on Snowflake?
- Converting pandas Code to pandas on Snowflake
- Working with pandas on Snowflake
- Hybrid Execution
Exam & Certification
Training & Certification Guide
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