Snowflake SQL for Data Analysis and Reporting Training in White House, Tennessee

Module 1: SQL Foundations

Basic Query Structure

Filtering Data

Aggregations

DISTINCT vs COUNT(DISTINCT)

Aliasing

Hands-On Labs

Lab 1:

Query customer data

Filter records

Sort results

Lab 2:

Aggregate sales data

Compare COUNT(*) vs COUNT(DISTINCT)

Module 2: Joins and Avoiding Fan-Out

INNER JOIN

LEFT JOIN

RIGHT JOIN

FULL OUTER JOIN

Multi-Condition Joins

The Fan-Out Problem (Critical Topic)

Hands-On Labs

Lab 3:

Build each join type

Lab 4:

Intentionally create fan-out

Diagnose incorrect totals

Repair query

Module 3: Data Types and Casting

Common Data Types

CAST

CAST(value AS NUMBER)

Snowflake Shorthand - value::NUMBER

Date Conversions

Time Zones

Hands-On Labs

Lab 5:

Convert strings to dates

Convert timestamps

Adjust time zones

Module 4: String and Date Functions

String Functions

Pattern Matching

Date Functions

Business Use Cases

Hands-On Labs

Lab 6:

Build reporting periods

Parse and clean text values

Module 5: Conditional Logic

CASE

Snowflake IFF

NULL Handling

COALESCE()

NULLIF()

NVL()

Hands-On Labs

Lab 7:

Categorize customers

Replace NULL values

Build reporting flags

Module 6: CTEs, Subqueries, and Window Functions

Common Table Expressions

Nested CTEs

Subqueries

Window Functions

Understanding Windows

Ranking Functions

Comparison Functions

Value Functions

PARTITION BY vs ORDER BY

DISTINCT vs Window-Based Deduplication

Hands-On Labs

Lab 8:

Top-N reports

Latest record selection

Period-over-period comparisons

Module 7: Snowflake QUALIFY

Traditional approach

Snowflake approach

Hands-On Labs

Lab 9:

Deduplicate customer records

Select latest transaction

Module 8: Query Performance and Best Practices

Why Duplicates Happen

Filter Early

Avoid SELECT *

ORDER BY Costs

Partition Pruning

Hands-On Labs

Lab 10:

Compare efficient and inefficient queries

Observe scan reductions

Module 9: Good SQL Habits

Formatting Standards

Meaningful Aliases

Effective Comments

Validation Techniques

Capstone Lab (Final Exercise)

Students build an executive sales report that includes:

Multiple joins

Date filtering

Aggregations

Window functions

QUALIFY

Ranking

Deduplication

Performance review

Students must identify and correct an intentional fan-out issue before producing final metrics.