Overview

This three‐day course is designed for professionals new to Snowflake. It introduces the fundamental concepts of the Snowflake AI Data Cloud and gradually builds toward implementing data engineering workflows using Python.

You begin with the basics—learning how Snowflake works, its architecture, and core features—before progressing to experiential activities with the Snowflake Python API and Snowpark.

The course blends instructor‐led lectures, demonstrations, interactive labs, and real‐world discussions to ensure a practical and accessible learning experience.

Skills Covered

  • Explain the distinctive features of Snowflake’s platform and its integration with Python.
  • Configure and establish secure connections to Snowflake using the Snowpark Session object.
  • Design, code, and deploy custom Python functions within Snowflake as User Defined Functions (UDFs).
  • Create and encapsulate reusable logic using Stored Procedures.
  • Organize and manage automated workflows with Snowflake tasks and Directed Acyclic Graphs (DAGs).
  • Automate recurring data tasks using Snowflake’s task scheduling capabilities.
  • Monitor and debug data processes while implementing observability techniques in Snowflake and Python environments.
  • Leverage Anaconda integration in Snowflake to enhance data solutions with specialized Python libraries.

Prerequisites

  • Recommended completion of the “Snowflake Multi-Factor Authentication (MFA) Essentials” free on-demand course.
  • Basic Python coding proficiency.
  • Familiarity with basic SQL.

Target Audience

  • Data Engineers
  • Data Scientists
  • Data Application Developers
  • Database Architects
  • Database Administrators
  • Data Analysts with programming experience

Course Curriculum

Snowflake AI Data Cloud

  • Storage Layer
  • Compute Layer
  • Cloud Service Layer
  • Using Snowsight
  • Snowflake Structure

Snowflake Python API

  • Python API Concepts
  • Core Classes and Operations

Data Protection Features

  • Cloning
  • Time Travel

Metadata and Caching in Snowflake

  • Metadata
  • Query Result Cache
  • Data Cache

Supporting Platform Features

  • Snowpark
  • Snowflake Connector for Python
  • Drivers, Clients, and Connectors Overview
  • Snowflake Notebook API

Ingestion

  • Data Loading Objects
  • Transformations and Copy Options
  • Bulk vs. Continuous Data Loading Approaches
  • Semi-structured Data
  • Snowpipe
  • Snowflake Data Loading Best Practices
  • Loading Semi-structured Data

Transformation

  • Creating and Managing Streams
  • UDFs and Stored Procedures
  • External Network Access
  • Working with Unstructured Data

Orchestration

  • Creating Tasks
  • Creating a DAG
  • Dynamic Tables

Delivery

  • Streamlit
  • Data Sharing

Management and Observability

  • Observability on Snowflake
  • Outbound Notifications
  • Snowflake Alerts
  • Data Pipeline Logging

Dates & Locations

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