Snowflake Fundamentals

Duration: 4 days
Modality: Virtual / ILT
Platform: Snowflake
Level: Introduction

Overview

Snowflake is a cloud-based data warehouse and analytical tool. It works on a new SQL engine best suited for the cloud.

Snowflake has one of the best ACID (atomicity, consistency, isolation, and durability) compliant solutions. One of Snowflake's unique capabilities is its table structures wherein Micro–Partitions and Data–Clustering is adopted. 

Audience

The audience for this class is Data Analysts, Data Engineers, Data Scientists, Data Architects, and Database Administrators.

Attendees should have SQL, Database, and Data Warehouse knowledge.

What you learn:

Module: Data Warehousing Overview

a. Data warehousing evolution
b. Cloud data warehousing
c. Adapting to increasing demands for data access and analytics

Module: Architecture and Overview

a. Technical Overview
b. Cloud Services Layer
c. Compute Layer
d. Storage Layer

Module: Architecture Deep Dive

a. Optimization
b. Metadata
c. Governance
d. Security
e. Transactions
f. Resource Management
g. Activity Monitoring
h. Sharing

Module: Data Movement

a. Data Loading
b. Data Unloading
c. External and Internal Stage objects
d. Snowpipe
e. Streams and Tasks
f. Best Practices

Module: Data Modeling and Table Types

a. Choosing the Right Table Type: Permanent, Temporary, Transient
b. Materialized Views
c. Temporary objects (views, functions, procedures)
d. Dynamic Tables (automated refresh pipelines)
e. Iceberg Tables (external cloud storage integration)
f. Partitioning and clustering strategies
g. Evolving schemas and document changes
h. Trade-offs between normalization, performance, and flexibility
i. Data Retention Policies

Module: Objects and Commands

a. Query Constructs
b. Data Description Language (DDL)
c. Data Manipulation Language (DML)
d. Metadata Commands

Module: Advanced SQL for Analytics

a. SQL Support and Query Best Practices
b. Analytic Functions Deep Dive (with Lab)
c. UDF and Stored Procedure (with Lab)
d. Performance Considerations and Use Cases
e. Query Acceleration Service (QAS)

Module: Managing Security

a. Data Encryption
b. Authentication
c. Role-Based Access Control
d. External Validation
e. Data Availability

Module: Semi-structured data

a. Working with semi-structured data
b. Queries
c. Data Optimization

Module: Caching

a. Caching Features
b. Performance Improvements
c. Cost Optimization

Module: Modern Data Sharing

a. Data Analytics as a Service
b. External Data Sharing
c. Monetizing Data
d. Sharing Data Sets

Module: Clients and Ecosystem

a. Clients
b. Connectors
c. SnowSQL

Module: Data Protection

a. Continuous Data Protection
b. Fail Safe
c. Time Travel
d. Cloning

Module: Performance and Concurrency

a. Query Profile
b. Micro-Partitions
c. Data Clustering
d. Scaling a Virtual Warehouse

Module: Account and Resources Management and Monitoring

a. System Resource Usage and Billing
b. Managing Virtual Warehouses
c. Workload Independence and Segmentation
d. Resource Monitors
e. Information Schema and Account Usage

Module: Certification Prep

a. Scope
b. Tips
c. Sample test
d. Additional Resources