Introduction

Snowflake is a leading cloud-based data warehousing platform that provides a flexible, scalable, and highly efficient solution for data storage, processing, and analysis. Built for the cloud, Snowflake allows users to easily integrate, manage, and analyze structured and semi-structured data without the complexity of traditional on-premise data warehouses. This training course is designed to provide participants with the skills and knowledge required to work with Snowflake, enabling them to build and manage modern cloud data warehouses for analytics, reporting, and business intelligence.

This course will cover Snowflake’s architecture, key features, and tools, with hands-on training to help participants understand how to use Snowflake for data ingestion, transformation, querying, and integration with other tools and platforms.

Objectives

By the end of this course, participants will:

• Understand the core concepts of cloud data warehousing and Snowflake architecture.
• Learn how to design and create databases, tables, and schemas in Snowflake.
• Gain experience with Snowflake’s data ingestion techniques (batch and streaming).
• Develop skills in performing complex SQL queries and data transformations in Snowflake.
• Learn how to scale Snowflake environments for performance optimization.
• Integrate Snowflake with third-party tools, data pipelines, and cloud platforms.
• Understand data security best practices for managing sensitive information.
• Implement data sharing and collaboration features available in Snowflake.
• Gain hands-on experience with real-world use cases and business intelligence workflows in Snowflake.

Who Should Attend?

This course is ideal for:

• Data engineers, data architects, and system administrators responsible for designing and managing data warehousing solutions.
• Business intelligence (BI) professionals, data analysts, and data scientists who need to access and analyze data in a cloud data warehouse.
• IT professionals seeking to learn about cloud-native data warehousing technologies.
• Cloud architects and developers looking to integrate Snowflake with other cloud services and data tools.
• Anyone interested in understanding and utilizing Snowflake for cloud-based data warehousing and analytics.

Day 1: Introduction to Cloud Data Warehousing and Snowflake

Morning Session: Overview of Cloud Data Warehousing

• Understanding cloud data warehousing concepts.
• The evolution of traditional on-premise data warehouses vs. cloud data warehousing.
• Introduction to Snowflake: What makes Snowflake different from other cloud data platforms (e.g., Amazon Redshift, Google BigQuery)?
• Key benefits of using Snowflake: Elasticity, scalability, and performance.
• Snowflake’s architecture: Virtual Warehouses, Databases, Schemas, and Tables.
• Hands-on: Creating a Snowflake account and navigating the web UI.

Afternoon Session: Snowflake Architecture and Core Components

• Snowflake’s multi-cloud architecture: AWS, Azure, and Google Cloud.
• Virtual Warehouses: The compute layer in Snowflake for query processing.
• Snowflake databases and schemas: Organizing data in Snowflake.
• Storage layer: How Snowflake stores structured and semi-structured data in its cloud storage.
• Understanding Snowflake’s shared data architecture and its benefits.
• Hands-on: Creating databases, schemas, and tables in Snowflake.

Day 2: Data Ingestion and Transformation in Snowflake

Morning Session: Loading Data into Snowflake

• Overview of data loading methods in Snowflake: Bulk loading, continuous data loading, and manual loading.
• Using the Snowflake Web UI, SnowSQL, and third-party ETL tools to load data into Snowflake.
• Loading structured data (CSV, Parquet, JSON) from cloud storage (e.g., AWS S3, Azure Blob Storage).
• Hands-on: Loading a dataset from AWS S3 into Snowflake using the COPY INTO command.

Afternoon Session: Transforming Data with Snowflake

• Data transformation using SQL in Snowflake: Common SQL commands for transforming data.
• Using Snowflake’s Streams and Tasks for data transformation and real-time processing.
• Working with semi-structured data (JSON, Avro, Parquet) in Snowflake: Native support for semi-structured formats.
• Hands-on: Creating data transformation workflows using SQL, Streams, and Tasks.

Day 3: Querying and Analyzing Data in Snowflake

Morning Session: Advanced SQL Queries in Snowflake

• Writing complex SQL queries in Snowflake: Joins, subqueries, and window functions.
• Using Snowflake’s Time Travel feature for querying historical data.
• Understanding Clustering Keys and how they can improve query performance.
• Query optimization in Snowflake: Best practices for optimizing query performance and reducing costs.
• Hands-on: Writing advanced queries to analyze data, using joins and window functions.

Afternoon Session: Performance Optimization and Scaling

• Scaling compute resources in Snowflake: Virtual Warehouses and how to adjust size for performance.
• Automatic scaling: How Snowflake automatically adjusts resources to meet demand.
• Query optimization strategies: Caching, result set caching, and query profiling.
• Using Materialized Views for faster query results.
• Hands-on: Optimizing a complex query in Snowflake and testing its performance.

Day 4: Integrating Snowflake with External Tools and Data Sharing

Morning Session: Integrating Snowflake with Third-Party Tools

• Integrating Snowflake with Business Intelligence (BI) tools: Tableau, Power BI, Looker.
• Using Snowflake with ETL tools: Talend, Apache NiFi, and Fivetran for data integration.
• Data exchange and API integrations with Snowflake.
• Introduction to Snowflake’s Data Marketplace and External Tables for accessing external data.
• Hands-on: Connecting Snowflake to Tableau for live data analysis.

Afternoon Session: Data Sharing and Collaboration in Snowflake

• Snowflake’s Data Sharing feature: How to securely share data across different Snowflake accounts.
• Snowflake’s secure data exchange model: Collaboration with external partners, departments, and organizations.
• Managing access to shared data using Secure Views and Role-Based Access Control (RBAC).
• Hands-on: Sharing data with a partner and setting access permissions.

Day 5: Data Security, Best Practices, and Real-World Use Cases

Morning Session: Data Security and Governance in Snowflake

• Understanding Snowflake’s data security model: Encryption, authentication, and authorization.
• Managing users, roles, and privileges with Role-Based Access Control (RBAC).
• Implementing data masking and data encryption to protect sensitive data.
• Snowflake’s Compliance Certifications: HIPAA, PCI DSS, SOC 2, and others.
• Hands-on: Implementing RBAC and secure data masking for sensitive columns.

Afternoon Session: Real-World Use Cases and Best Practices

• Real-world use cases for Snowflake: Data lakes, business intelligence, real-time analytics, and machine learning.
• Best practices for managing and optimizing Snowflake environments in production.
• Scaling Snowflake for large workloads and cost optimization strategies.
• Managing Snowflake’s data pipelines and automation with Snowflake Tasks and Streams.
• Hands-on: Building an end-to-end data pipeline using Snowflake and SnowSQL.

Materials and Tools:

• Software: Snowflake platform, SnowSQL, Tableau, Power BI, and other BI tools.
• Datasets: Sample datasets for loading and querying (e.g., transactional data, customer data).
• Recommended Reading: “The Snowflake Cloud Data Platform” documentation and best practices guides.

Post-Course Support:

• Access to course materials, recorded sessions, and discussion forums for ongoing learning.
• Practical exercises and a final project focused on building a cloud data warehouse solution using Snowflake.
• Continuing support through community forums and expert Q&A sessions.