Introduction

In today’s fast-paced business environment, organizations require scalable, flexible, and cost-effective data warehousing solutions to handle vast amounts of data efficiently. Snowflake has emerged as a cloud-native, high-performance data warehouse that simplifies data storage, processing, and analytics across multiple cloud platforms.

This 5-day hands-on training course provides an in-depth understanding of Snowflake’s architecture, key features, and advanced capabilities. Participants will learn how to design, implement, and optimize modern data warehousing solutions using Snowflake’s multi-cluster architecture, data sharing, security, and AI-driven analytics.

Objectives

By the end of this course, participants will be able to:

• Understand Snowflake’s architecture and how it differs from traditional data warehouses.
• Implement data modeling best practices for efficient data storage and querying.
• Load, transform, and manage structured and semi-structured data in Snowflake.
• Optimize performance, scalability, and cost efficiency in Snowflake.
• Implement advanced security, access control, and compliance measures.
• Integrate BI tools, machine learning, and real-time analytics with Snowflake.
• Leverage Snowflake’s advanced features, such as Data Sharing, Data Marketplace, and Snowpark.
• Understand future trends in cloud-based data warehousing.

Who Should Attend?

This course is ideal for:

• Data Engineers building and managing modern cloud data warehouses.
• Data Architects designing scalable and cost-effective data solutions.
• Business Intelligence (BI) Professionals working with analytics and reporting.
• Database Administrators (DBAs) migrating to Snowflake.
• Cloud Engineers & DevOps Teams integrating Snowflake with cloud services.
• Data Scientists & AI Engineers leveraging Snowflake for machine learning and AI-driven insights.
• IT Managers & CTOs strategizing cloud data initiatives.

Day 1: Introduction to Snowflake & Cloud Data Warehousing

• Cloud Data Warehousing Overview

  • Evolution from on-premises to cloud data warehouses
  • Benefits of Snowflake over traditional databases
  • Snowflake vs. Redshift, BigQuery, and Azure Synapse

• Snowflake Architecture & Key Concepts

  • Multi-cluster shared data architecture
  • Separation of storage, compute, and services layers
  • Virtual warehouses, query execution, and auto-scaling

• Setting Up Snowflake

  • Provisioning a Snowflake account
  • Understanding Snowflake editions (Standard, Enterprise, Business Critical)
  • Navigating the Snowflake UI and basic SQL commands

• Hands-on Lab:

  • Creating a Snowflake account and setting up a workspace

Day 2: Data Loading, Storage & Optimization

• Loading Data into Snowflake

  • Bulk loading structured data (CSV, JSON, Avro, Parquet)
  • Streaming and real-time ingestion (Kafka, Snowpipe)
  • Staging files (Internal vs. External stages)

• Data Storage & Partitioning

  • Understanding micro-partitions and columnar storage
  • Clustering and partitioning strategies for performance tuning
  • Using Time Travel and Fail-safe for data recovery

• Query Performance Optimization

  • Best practices for writing efficient queries
  • Understanding pruning, caching, and auto-scaling
  • Materialized views and clustering keys

• Hands-on Lab:

  • Loading structured and semi-structured data into Snowflake
  • Query optimization and performance tuning

Day 3: Security, Access Control & Governance

• User Management & Role-Based Access Control (RBAC)

  • Snowflake’s role hierarchy and privileges
  • Best practices for securing Snowflake accounts
  • Using Multi-Factor Authentication (MFA) and Single Sign-On (SSO)

• Data Governance & Compliance

  • Implementing data masking and row-level security
  • Column-level security for sensitive data
  • Compliance with GDPR, HIPAA, and SOC 2

• Encryption & Data Protection

  • How Snowflake encrypts data in transit and at rest
  • Managing key-based access controls and auditing logs
  • Monitoring user activity with Snowflake Access History

• Hands-on Lab:

  • Implementing role-based access control and data masking

Day 4: Advanced Snowflake Features & BI Integration

• Data Sharing & Snowflake Marketplace

  • Cross-account secure data sharing
  • Real-time collaboration with Snowflake Data Exchange
  • Exploring external data via the Snowflake Marketplace

• Integrating Snowflake with BI & Analytics Tools

  • Connecting to Tableau, Power BI, and Looker
  • Running analytics workloads with dbt and Sigma Computing
  • Building dashboards and real-time reporting solutions

• Machine Learning & AI in Snowflake

  • Introduction to Snowpark for Python-based data science
  • Using SQL-based ML models with Snowflake ML
  • Integrating Snowflake with AWS SageMaker, Databricks, and Google AI

• Hands-on Lab:

  • Connecting Snowflake to a BI tool and building dashboards
  • Running a machine learning model using Snowpark

Day 5: Future Trends, Best Practices & Final Project

• Future of Cloud Data Warehousing

  • The rise of Data Lakehouses
  • AI-powered automation in data management
  • Emerging technologies in cloud analytics

• Best Practices for Snowflake Implementation

  • Designing cost-efficient architectures
  • Scaling Snowflake for high-performance workloads
  • Avoiding common pitfalls and performance bottlenecks

• Final Project: End-to-End Data Warehousing Solution

  • Design and implement a real-world data warehouse using Snowflake
  • Apply best practices in governance, security, and analytics

• Course Wrap-Up & Certification

  • Review of key concepts
  • Q&A and discussions on real-world challenges
  • Certification of completion