#Snowflake Data Engineer

A Snowflake Data Engineer specializes in building and managing scalable data pipelines using the Snowflake Data Cloud platform. They handle data ingestion, transformation, and optimization for analytics and reporting. Engineers use SQL, Snowpipe, Streams, and Tasks to automate and manage workflows. This role is in high demand for cloud-based data engineering and modern data warehouse solutions.

Training Schedules

S No	Time (IST, Mon - Fri)	Start Date
1	9:30 AM - 10:30 AM	Oct 2nd
2	8 AM - 9 AM	Sep 13th

Snowflake Data Engineer
Course Contents:

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Module 1: MSSQL & TSQL Queries

Ch 1: SQL SERVER INTRODUCTION

Database Introduction
Types of Databases
Need for & ETL, DWH
BI Implementations
SQL Server Advantages
Version, Editions of MSSQL
Data Analyst Job Roles

Ch 2: SQL SERVER INSTALLATIONS

SQL Server 2019, 2017
SSMS Tools Installation
Database Engine (OLTP)
SCM, Configuration Tools
Instance Types, Uses
Authentication Modes
Collation, File Stream

Ch 3: SQL BASICS – 1

Need for Databases, Tables
Need for SQL Commands
DDL, DML & DQL Statements
Database Creation @ GUI
Data Operations @ GUI
Session ID, SQL Context
DB, Tables, Data @ SQL

Ch 4: SQL BASICS – 2

DDL Variants in MSSQL
DML Variants in MSSQL
INSERT & INSERT INTO
SELECT & SELECT INTO
Basic Operators in SQL
Special Operators in MSSQL
ALTER, ADD, TRUNCATE, DROP

Ch 5: Data Imports, Schemas

Data Imports with Excel
ORDER BY & UNION
UNION ALL For Sorting Data
Creating, Using Schemas
Real-world Banking Database
Table Migrations @ Schemas
2 Part, 3 Part & 4 Part Naming

Ch 6 : Constraints, Index Basics

Need for Constraints, Keys
NULL, NOT NULL, UNIQUE
Primary Key & Foreign Key
RDBMS and ER Models
Identity Property, Default
Clustered Index, Primary Key
Non Clustered Index, Unique

Ch 7: Joins & Views Basics

JOINS: Purpose. Inner Joins
Left / Right / Full Outer Joins
Cross Joins, Query Tuning
Creating & Using Views
DML, SELECT with Views
RLS : WITH CHECK OPTION
System Views & Metadata

Ch 8: Functions(UDF), Data Types

Using Functions in MSSQL
Scalar Value Functions
Inline & Multiline Functions
Date & Time Functions
String, Aggregate Functions
Data Types : Integer, Char, Bit
SQL Variant, Timestamp, Date

Ch 9: Stored Procedures,Models

Stored Procedures & Usage
Creating, Testing Procedures
Encryption, Deferred Names
SPs for Validations, Analysis
System SPs, Recompilation
Normal Forms & Types
Data Models, Self-References

Ch 10: Triggers, Temp Tables

Need for Triggers
DDL & DML Triggers
Using Memory Tables
Data Replication, Automation
Local & Global Temp Tables
Testing & Using Temp Tables
SELECT .. INTO & Bulk Loads

Ch 11: DB Architecture, Locks

Planning VLDBs : Files, Sizing
Filegroups, Extents & Types
Log Files : VLF, Mini LSN
Table Location, Performance
Schemas, Transfer, Synonyms
Transactions Types, Lock Hint
Query Blocking Scenarios

Ch 12 : Cursors & CTEs, Links

Cursors : Realtime Use
Fetch & Access Cursor Rows
CTEs for SELECT, DML
CTEs: Scenarios & Tuning
Linked Servers, Remote Joins
Linked Servers: MSDTC, RPC
Tuning Remote Queries

Ch 13: Merge, Upsert & Rank

Need for Merge in ETL
Incremental Loads with SQL
MERGE and RANK Functions
Window Functions, Partition
Identify, Remove Duplicates

Ch 14: Grouping & Cube

Group By & HAVING
Cube, Rollup & Grouping
Joins with Group By
3 Table, 4 Table Joins
Query Execution Order

Ch 15: Self Joins, Excel Analysis

Self Joins & Self References
UNION, UNION ALL
Sub Queries with Joins
IIF, CASE, EXISTS Statements
Excel Analytics, Pivot Reports

Module 2: Snowflake

Ch 1: Introduction to Snowflake

Database, DWH Introduction
OLTP, OLAP and DWH
Data Warehouse Advantages
Cloud DWH Implementations
IaaS, PaaS & SaaS Concepts
Snowflake Cloud Intro
Snowflake: SaaS Platform
Snowflake Advantages

Ch 2: Snowflake Concepts

Snowflake Account (Cloud)
Snowflake Components
Snowflake Editions, Credits
Web UI & Snow Sight
Snowflake Editions
Snowflake Storage
Virtual Private Edition (VPS)
Snowflake Pricing

Ch 3: Architecture, Warehouse

Compute Architecture
Shared Disk Architecture
Nodes and Clusters
CPU & Memory in Clusters
Database Query & Data Cycle
ColumnStore,Virtual Warehouse
Classic UI with Snowflake
Massively Parallel Processing

Ch 4: Snowflake DB & Tables

DB Objects and Hierarchy
Worksheet Parameters
DB Creation with Snowflake
Snowflake Tables and Usage
Retention Time, Connections
Permanent, Transient Types
CREATE TABLE AS SELECT
ALTER, DROP & UNDROP

Ch 5: Time Travel, Recovery

Time Travel in Snowflake
Continuous Data Protection
Invoking Time Travel Feature
Timestamp, Offset, Query ID
Data Recovery, TIMESTAMP
OFFSET in Real-world
Fail Safe and UNDROP
Transient Tables, Real-time

Ch 6: Schemas and Session Context

Schema Creation Usage
Permanent, Transient Schemas
Managed Schemas in Snowflake
Invoking Schemas & Cloning
Snowflake Workspaces
Session Context & Schema
Query and History Tab in GUI
Data Loading with GUI

Ch 7: Constraints & Data Types

Constraints & Validations
NULL, NOT NULL Properties
Keys & Constraints, Usage
Inline, Out Of LineConstraints
Table Constraints, Use
ENFORCED Constraints
DEFERRED, IMMEDIATE
Snowflake Data Types

Ch 8: Snowflake Cloning

Cloning with Snowflake
Zero Copy, Schema Cloning
Real-time Cloning
Snapshot, Metadata
Permissions for Cloning
Accessing, Clone
Storage & Metadata Layer
Real-time Considerations

Ch 9: Snowflake Procedures

Procedures and Functions
SQL and JavaScript & CALL
Transactions & Injection
sqlText:command
Cursoring Data and Operations
Dynamic DML with SPs
Working with Loops
RETURN, RETURNS Statements

Ch 10: Security Management

Security with Snowflake
Users & Roles in Snowflake
Privileges and Groups
Organization, Account, Users
Creating, Using Roles, Users
System Defined Roles Usage
Role Hierarchy, Dependency
RBAC & DAC in Real-time

Ch 11: Snowflake Transactions

Transaction ACID Properties
Implicit, Explicit and Auto
Durability and Data Storage
current_transaction() Usage
to_timestamp_ltz and Usage
Failed Transactions with SPs
Transactions and SPs
Scoped & INNER Transactions

Ch 12: Snowflake Streams & Audits

Snowflake Streams & Usage
Streams and DML Auditing
Snapshot Creation, Offset
METADATA Options & Streams
Auditing DML Operations
Data Flow & Snowflake Streams
Streams on Transient Tables
Time Travel with Stream Tables

Ch 13: Snowflake Tasks, Partitions

Tasks, Serverless Compute
Tasks Tree: Root and DAG
Tasks Schedules and RESUME
User & Snowflake Managed
CRON Syntax with Tasks
Virtual Warehouse Concepts
Multi Cluster Warehouse
Auto Scale Options, Billing

Ch 14: SnowSQL and Variables

SnowSQL Configurations
DDL, DML & SELECT
SnowSQL Command Line
Variables and Batch Process
DECLARE, LET, BEGIN & END
EXECUTE IMMEDIATE, FOR
Creating Virtual Warehouse
Writing Output to Files

Ch 15: Snowflake Partitions, Stages

Snowflake Partitions, Use
Micro Partition with DML, CDC
Cluster Key, Depth and Overlap
Internal Partition Types & Usage
List, Range and Hash Partitions
Snowflake Stages, Types
Internal and External Stages
COPY Command, Bulk Loads

Ch 16: Azure & External Stages

Azure Subscriptions
Azure Storage Account, BLOB
SAS: Shared Access Signature
Using SAS Key and FILE PATH
Azure Storage with BLOB
COPY INTO Command Usage
Snowflake Patterns & RegEx
File Formats: Creation, Usage

Ch 18: Realtime Case Study

Module 3: Data Build Tool

Ch 1 : DBT Fundamentals

What is Data Build Tool?
DBT as a data transformation tool
Importance of DBT in ELT workflows
DBT Cloud for data transformations

Ch 2: DBT Models and Materializations

Building models in DBT
Types of materializations
Table, view, incremental materializations
Model configurations

Ch 3: DBT Jinja Templating

Introduction to Jinja
Using Jinja with DBT
Macros and reusable code
Implementing dynamic SQL

Ch 4: DBT Testing and Documentation

Writing and executing tests
Data quality checks
DBT documentation and lineage graphs
Generating DBT docs

Ch 5: DBT Seeds and Sources

Using seeds for static data
Defining and using sources
Source freshness checks
Integrating external data

Ch 6: DBT Deployment and CI/CD

Deployment strategies for DBT
Continuous integration and deployment
Automating DBT workflows
Version control with Git

Ch 7: DBT Best Practices

Project structure recommendations
Coding standards and guidelines
DBT project optimization
Performance tuning tips

Ch 8: Hooks in DBT

Custom scripts to run at specific points
Adding additional logic to streamline Snowflake
Analyses and exploratory data workflows
Ad-hoc analyses that do not get materialized

Ch 9: DBT Snapshots

Managing historical data
Implementing DBT snapshots
Snapshot configuration
Strategies for handling changes

Ch 10: DBT Packages and Extensions

Leveraging DBT packages
Using community packages
Extending DBT functionality
✓ Integrations with other data tools

Ch 11: DBT Advanced Topics

Advanced Jinja usage, Snowpark
Handling complex data scenarios
Custom materializations, Snowpark
Troubleshooting and debugging techniques

Ch 12: Real-time Project Phase 1

Defining project requirements
Initial project setup and DBT configuration
Model planning and development
Initial testing and validation

Ch 13: Real-time Project Phase 2

Project deployment and monitoring
Implementing advanced DBT features
Comprehensive testing and documentation
Real-world deployment considerations

SQL SCHOOL

24x7 LIVE Online Server (Lab) with Real-time Databases.
Course includes ONE Real-time Project.

Snowflake Data Engineer Training FAQs

What is Snowflake Data Engineer Job Role?

A Python Full Course is a complete training program that teaches you how to write Python code, build applications, automate tasks, and work with data.

🔑 Key Responsibilities:

Develop software applications and REST APIs
Write clean, efficient, and reusable Python code
Automate workflows, tasks, and data processes
Work with frameworks like Django, Flask, or FastAPI
Collaborate with teams for integration, testing, and deployment

What are the Job Roles of an Snowflake Data Engineer?

💼 Top Job Roles:

✅ Data Pipeline Developer
✅ Snowflake SQL Developer
✅ Cloud Data Warehouse Engineer
✅ Data Integration Specialist
✅ ETL/ELT Workflow Designer
✅ Performance Tuning Expert
✅ Snowflake Security & Access Manager
✅ Data Modeling Specialist
✅ Data Quality & Validation Analyst
✅ Real-Time Data Processing Engineer

What does our Snowflake Data Engineer Training course contains?

The course is carefully curated with below module:
👉🏻Module 1: MSSQL & TSQL Queries
👉🏻Module 2: Snowflake
👉🏻Module 3: Data Build Tool

Who can join this course?

✅ Freshers aiming to start a career in cloud data engineering
✅ Working professionals looking to shift to Snowflake or modern ETL roles
✅ ETL & SQL developers upgrading to cloud data platforms
✅ Students from any background interested in data and analytics
✅ IT & Non-IT professionals planning to upskill for better job roles
✅ Anyone with basic computer knowledge and a passion to learn

No prior Snowflake or cloud experience required – training starts from basics.

What training modes are available?

Option 1: LIVE Online Training (100% Interactive, step by step, assignments)

Option 2: Self Paced Videos (100% practical, step by step with concept wise assignments)

You may choose any one of these options, same curriculum!

I (Trainer) shall be available for doubts and clarifications, assignment check and review.

Why should I choose SQL School for Snowflake Data Engineer training?

👉🏻 Every session is Practical, Step by Step with Concept wise FAQs !!

👉🏻 100% results with on-time practice. Daily Tasks for every session.

👉🏻 Concept wise tasks be submitted before next class for Job Waiters / Starters.

👉🏻 Concept wise tasks due for submission by Weekends for Working Professionals.

Why Choose SQL School

100% Real-Time and Practical
ISO 9001:2008 Certified
Concept wise FAQs
TWO Real-time Case Studies, One Project
Weekly Mock Interviews
24/7 LIVE Server Access
Realtime Project FAQs
Course Completion Certificate
Placement Assistance
Job Support
Realtime Project Solution
MS Certification Guidance