Live Online Snowflake Course for Data Engineering

Live Course Module: Snowflake Course for Data Engineering

Total Duration: 40 Hours (4 Weeks)

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WEEK 1: Introduction to Snowflake & Data Warehousing Basics

Duration: 8 Hours (4 Sessions × 2 Hrs)**

Topics:

1. Introduction to Cloud Data Warehousing (2 hrs)

   • What is Data Warehousing?

   • Traditional vs Cloud Data Warehousing

   • Snowflake Architecture & Key Features (Storage, Compute, Services Layers)

2. Snowflake Account Setup & UI Overview (2 hrs)

   • Creating a Snowflake trial account

   • Understanding Web UI, Worksheets, and Databases

   • Setting up roles, warehouses, and schemas

3. Data Loading & Unloading (2 hrs)

   • Loading data using COPY command

   • Working with internal and external stages

   • Unloading data to cloud storage (S3, Azure Blob, GCS)

4. Mini Project + Q&A (2 hrs)

   • Load and query sample data into Snowflake from cloud storage

Learning Outcome:

✅ Understand Snowflake architecture & setup
✅ Create and manage warehouses, databases, and roles
✅ Perform basic data loading and querying operations

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WEEK 2: Working with Snowflake SQL & Data Modeling

Duration: 10 Hours (5 Sessions × 2 Hrs)**

Topics:

1. Core SQL in Snowflake (2 hrs)

   • DDL, DML, and DQL operations

   • Working with Views, Temporary Tables, and CTEs

2. Snowflake Functions & Expressions (2 hrs)

   • Built-in functions for string, date, numeric, and aggregation

   • Conditional expressions and CASE statements

3. Data Modeling in Snowflake (2 hrs)

   • Star vs Snowflake Schema design

   • Implementing dimensional modeling

   • Best practices for warehouse schema design

4. Performance Optimization (2 hrs)

   • Query profiling and result caching

   • Clustering keys, micro-partitions, and pruning

   • Warehouse sizing and cost management

5. Mini Project + Q&A (2 hrs)

   • Build and optimize a simple data mart using Snowflake SQL

Learning Outcome:

✅ Write efficient Snowflake SQL queries
✅ Design optimized schemas for analytics
✅ Implement cost-efficient and performance-tuned queries

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WEEK 3: Data Integration, ETL/ELT, and Automation

Duration: 10 Hours (5 Sessions × 2 Hrs)**

Topics:

1. Integrating Snowflake with ETL Tools (2 hrs)

   • Overview of ETL/ELT processes

   • Connecting Snowflake with Apache Airflow, dbt, Talend, or Informatica

2. Using Snowpipe for Continuous Data Ingestion (2 hrs)

   • Snowpipe setup and automation

   • Working with Streams and Tasks

3. Data Sharing & Cloning (2 hrs)

   • Secure Data Sharing

   • Database and Schema Cloning

   • Zero-Copy Cloning for data reuse

4. Semi-Structured Data Handling (2 hrs)

   • Working with JSON, Avro, and Parquet

   • Flatten and Variant data types

   • Querying nested data

5. Mini Project + Q&A (2 hrs)

   • Build an automated ETL pipeline using Snowpipe and Streams

Learning Outcome:

✅ Automate data ingestion using Snowpipe and Streams
✅ Handle structured and semi-structured data
✅ Integrate Snowflake with ETL tools and external systems

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WEEK 4: Advanced Topics, Security, and Capstone Project

Duration: 12 Hours (6 Sessions × 2 Hrs)**

Topics:

1. Data Governance, Roles & Security (2 hrs)

   • Role-Based Access Control (RBAC)

   • Managing users, roles, and privileges

   • Data encryption and masking policies

2. Time Travel, Fail-Safe & Data Recovery (2 hrs)

   • Restoring historical data

   • Understanding Fail-Safe and Continuous Data Protection

3. Snowflake Integration with Cloud Platforms (2 hrs)

   • Using AWS S3, Azure Blob, and GCP Storage

   • Data sharing across regions and accounts

4. Performance Tuning & Monitoring (2 hrs)

   • Query history and monitoring tools

   • Resource monitors and cost optimization

5. Capstone Project Development (2 hrs)

   • Design a cloud-based data warehouse for analytics

   • Include ingestion, transformation, and reporting

6. Capstone Presentation & Review (2 hrs)

   • Project walkthrough & peer review

   • Instructor feedback and best practices

Learning Outcome:

✅ Implement governance, security, and recovery in Snowflake
✅ Monitor and optimize performance
✅ Deploy production-grade data pipelines using Snowflake

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🧩 CAPSTONE PROJECT EXAMPLE

Project Title: End-to-End Cloud Data Warehouse for Retail Analytics
Goal: Build a Snowflake-based data warehouse to ingest, transform, and analyze retail transactions from multiple sources.
Stack: Snowflake, Snowpipe, AWS S3, dbt, Airflow, Power BI/Tableau

FINAL COURSE OUTCOMES

By the end of this 4-week (40-hour) live training, you will be able to:

✅ Understand and implement Snowflake’s cloud data architecture
✅ Build and manage scalable, optimized data warehouses
✅ Integrate Snowflake with ETL/ELT tools and cloud services
✅ Handle semi-structured and streaming data efficiently
✅ Apply security, governance, and performance best practices
✅ Build and deploy a real-world Snowflake project for your portfolio