Learn SnowflakeDB

SnowflakeDB is a cloud-native SaaS data platform that supports ad-hoc, data warehouse and other workloads. It runs on AWS, Azure or GCP. To learn about the origins, see this wikipedia article - link.

This Repo is a companion to my LinkedIn Learning course "Learning SnowflakeDB" --> link

• SnowflakeDB's % of the cloud DW (data warehouse) market - link.
  • Compare - link
  • Pricing - link
  • Editions / feature matrix - link
  • Regions - link

---

Resources to work with SnowflakeDB

List of Related Links

• Awesome SnowflakeDB GitHub link list - link
• SnowflakeDB GitHub link list of demo notebooks - link

Articles

• 📝 Medium Article "Snowflake - the Data Cloud" (great infographics!) - link
• 📝 Getting started guide for SnowflakeDB - link
• SnowflakeDB open source tools on GitHub - link

Screencasts

• 📺 SnowflakeDB user guide - link
• 📺 8 min "What is SnowflakeDB?" demo - link
• 📺 15 min "How SnowflakeDB Stores Table Data" screencast - link
• 📺 SnowflakeDB YouTube channel - link

Latest SnowflakeDB Product and Feature Releases (2025)

• Focus on AI and AI-driven Solutions:
  • Ongoing developments in Agentic AI, particularly for Security Operations Centers (announced May 2025).
  • Snowflake highlights significant ROI from AI investments for early adopters (research published April 2025).
  • New AI-powered solutions for the Automotive industry (announced May 2025).
• Commitment to Open Standards and Data Governance:
  • Introduction of new Apache Iceberg™ innovations, enhancing open data capabilities and AI-readiness (announced April 2025).
• Enhanced Security and Compliance:
  • Achieved Department of Defense (DoD) IL5 Authorization, reinforcing its position for government and defense customers (announced April 2025).

Generative AI Capabilities

• Snowflake Cortex AI: Access Large Language Models (LLMs) and develop AI-powered applications and agents.
• Snowflake ML: Streamline machine learning model development with integrated MLOps capabilities.
• Snowpark: Train ML models using familiar languages like Python, Java, and Scala with seamless data access.
• Streamlit: Build and share interactive web applications for ML models and data insights directly from Python scripts.
• Document AI: Leverage intelligent document processing to extract insights from unstructured documents.
• Unstructured Data Handling: Efficiently manage and process various unstructured data types (images, audio, video, PDFs) for AI/ML workloads.
• Strategic Partnerships: Collaborate with industry leaders, such as Microsoft, to integrate services like OpenAI models.