Learn SnowflakeDB

May 23, 2025 · View on GitHub

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.

Resources to work with SnowflakeDB

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

Articles

  • 📝 Medium Article "Snowflake - the Data Cloud" (great infographics!) - link
  • 📝 Getting started guide for SnowflakeDB - link
  • :octocat: 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.